The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Stephane Doncieux - One of the best experts on this subject based on the ideXlab platform.
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How to Promote Generalisation in Evolutionary Robotics: the ProGAb Approach
2016Co-Authors: Tony Pinville, Sylvain Koos, Jean-baptiste Mouret, Stephane DoncieuxAbstract:In Evolutionary Robotics (ER), controllers are assessed in a single or a few environments. As a consequence, good performances in new di erent contexts are not guaranteed. While a lot of ER works deal with robustness, i.e. the ability to perform well on new contexts close to the ones used for evaluation, no current approach is able to promote broader generalisation abilities without any assumption on the new contexts. In this paper, we introduce the ProGAb approach, which is based on the standard three data sets methodology of supervised machine learning, and compare it to state-of- the-art ER methods on two simulated robotic tasks: a navi- gation task in a T-maze and a more complex ball-collecting task in an arena. In both applications, the ProGAb ap- proach: (1) produced controllers with better generalisation abilities than the other methods; (2) needed two to three times fewer evaluations to discover such solutions.
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Evolutionary Robotics: what, why, and where to
Frontiers in Robotics and AI, 2015Co-Authors: Stephane Doncieux, Jean-baptiste Mouret, Nicolas Bredeche, Gusz EibenAbstract:Evolutionary Robotics applies the selection, variation, and heredity principles of natural evolution to the design of robots with embodied intelligence. It can be considered as a subfield of Robotics that aims to create more robust and adaptive robots. A pivotal feature of the Evolutionary approach is that it considers the whole robot at once, and enables the exploitation of robot features in a holistic manner. Evolutionary Robotics can also be seen as an innovative approach to the study of evolution based on a new kind of experimentalism. The use of robots as a substrate can help to address questions that are difficult, if not impossible, to investigate through computer simulations or biological studies. In this paper, we consider the main achievements of Evolutionary Robotics, focusing particularly on its contributions to both engineering and biology. We briefly elaborate on methodological issues, review some of the most interesting findings, and discuss important open issues and promising avenues for future work.
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Beyond black-box optimization: a review of selective pressures for Evolutionary Robotics
Evolutionary Intelligence, 2014Co-Authors: Stephane Doncieux, Jean-baptiste MouretAbstract:Evolutionary Robotics is often viewed as the application of a family of black-box optimization algorithms -- Evolutionary algorithms - - to the design of robots, or parts of robots. When considering Evolutionary Robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most Evolutionary Robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because Evolutionary Robotics experiments share common features, selective pressures for Evolutionary Robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.
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encouraging behavioral diversity in Evolutionary Robotics an empirical study
Evolutionary Computation, 2012Co-Authors: Jean-baptiste Mouret, Stephane DoncieuxAbstract:Evolutionary Robotics (ER) aims at automatically designing robots or controllers of robots without having to describe their inner workings. To reach this goal, ER researchers primarily employ phenotypes that can lead to an infinite number of robot behaviors and fitness functions that only reward the achievement of the task-and not how to achieve it. These choices make ER particularly prone to premature convergence. To tackle this problem, several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic Evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation; however, it also introduces new questions: how to compare behavior? should this comparison be task specific? and what is the best way to encourage diversity in this context? In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework. We compare each approach on three different tasks and two different genotypes. The results show that fostering behavioral diversity substantially improves the Evolutionary process in the investigated experiments, regardless of genotype or task. Among the benchmarked approaches, multi-objective methods were the most efficient and the generic, Hamming-based, behavioral distance was at least as efficient as task specific behavioral metrics.
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The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics
IEEE Transactions on Evolutionary Computation, 2012Co-Authors: Sylvain Koos, Jean-baptiste Mouret, Stephane DoncieuxAbstract:The reality gap, that often makes controllers evolved in simulation inefficient once transferred onto the physical robot, remains a critical issue in Evolutionary Robotics (ER). We hypothesize that this gap highlights a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: the most efficient solutions in simulation often exploit badly modeled phenomena to achieve high fitness values with unrealistic behaviors. This hypothesis leads to the Transferability approach, a multi-objective formulation of ER in which two main objectives are optimized via a Pareto-based Multi-Objective Evolutionary Algorithm: (1) the fitness and (2) the transferability, estimated by a simulation-to-reality (STR) disparity measure. To evaluate this second objective, a surrogate model of the exact STR disparity is built during the optimization. This Transferability approach has been compared to two reality-based optimization methods, a noise-based approach inspired from Jakobis minimal simulation methodology and a local search approach. It has been validated on two robotic applications: 1) a navigation task with an e-puck robot; 2) a walking task with an 8-DOF quadrupedal robot. For both experimental set-ups, our approach successfully finds efficient and well-transferable controllers only with about ten experiments on the physical robot.
Stefano Nolfi - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Robotics simulations help explain why reciprocity is rare in nature
Scientific Reports, 2016Co-Authors: Jean-baptiste André, Stefano NolfiAbstract:The relative rarity of reciprocity in nature, contrary to theoretical predictions that it should be widespread, is currently one of the major puzzles in social evolution theory. Here we use Evolutionary Robotics to solve this puzzle. We show that models based on game theory are misleading because they neglect the mechanics of behavior. In a series of experiments with simulated robots controlled by artificial neural networks, we find that reciprocity does not evolve, and show that this results from a general constraint that likely also prevents it from evolving in the wild. Reciprocity can evolve if it requires very few mutations, as is usually assumed in Evolutionary game theoretic models, but not if, more realistically, it requires the accumulation of many adaptive mutations.
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selective attention enables action selection evidence from Evolutionary Robotics experiments
Adaptive Behavior, 2013Co-Authors: Giancarlo Petrosino, Domenico Parisi, Stefano NolfiAbstract:In this paper we investigate whether selective attention enables the development of action selection (i.e. the ability to select among conflicting actions afforded by the current agent/environmental context). By carrying out a series of experiments in which neuro-robots have been evolved for the ability to forage so to maximize the energy that can be extracted from ingested substances we observed that effective action and action selection capacities can be developed even in the absence of internal mechanisms specialized for action selection. However, the comparison of the results obtained in different experimental conditions in which the robots were or were not provided with internal modulatory connections demonstrate how selective attention enables the development of a more effective action selection capacity and of more effective and integrated action capacities.
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breedbot an Evolutionary Robotics application in digital content
The Electronic Library, 2008Co-Authors: Orazio Miglino, Onofrio Gigliotta, Michela Ponticorvo, Stefano NolfiAbstract:Purpose – This paper aims to describe an integrated hardware/software system based on Evolutionary Robotics and its application in an edutainment context.Design/methodology/approach – The system is based on a wide variety of artificial life techniques (artificial neural networks, genetic algorithms, user‐guided Evolutionary design and Evolutionary Robotics). A user without any computer programming skill can determine the robot's behavior in two different ways: artificial breeding or artificial evolution. Breedbot has been used as a didactic tool in teaching Evolutionary biology and as a “futuristic” toy by several science centers. The digital side of Breedbot can be downloaded on the web site: www.isl.unina.it/breedbot Findings – The results in this pilot study suggest that using Breedbot in an educational context can be useful to improve learning in biology.Research limitations/implications – As this is a pilot study, one limitation is the small sample considered. The issue will be investigated further w...
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toward open ended Evolutionary Robotics evolving elementary robotic units able to self assemble and self reproduce
Connection Science, 2004Co-Authors: Raffaele Bianco, Stefano NolfiAbstract:In this paper, we discuss the limitations of current Evolutionary Robotics models and we propose a new framework that might solve some of these problems and lead to an open-ended Evolutionary process in hardware. More specifically, the paper describes a novel approach where the usual concepts of population, generations and fitness are made implicit in the system. Individuals co-evolve embedded in their environment. Exploiting the self-assembling capabilities of the (simulated) robots, the genotype of a successful individual can spread in the population. In this way, interesting behaviours emerge spontaneously, resulting in chasing and evading other individuals, collective obstacle avoidance and co-ordinated motion of self-assembled structures.
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Evolutionary Robotics the biology intelligence and technology of self organizing machines
2000Co-Authors: Stefano Nolfi, Dario FloreanoAbstract:Evolutionary Robotics is a new technique for the automatic creation of autonomous robots. Inspired by the Darwinian principle of selective reproduction of the fittest, it views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention. Drawing heavily on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic engineering. The resulting robots share with simple biological systems the characteristics of robustness, simplicity, small size, flexibility, and modularity. In Evolutionary Robotics, an initial population of artificial chromosomes, each encoding the control system of a robot, is randomly created and put into the environment. Each robot is then free to act (move, look around, manipulate) according to its genetically specified controller while its performance on various tasks is automatically evaluated. The fittest robots then "reproduce" by swapping parts of their genetic material with small random mutations. The process is repeated until the "birth" of a robot that satisfies the performance criteria. This book describes the basic concepts and methodologies of Evolutionary Robotics and the results achieved so far. An important feature is the clear presentation of a set of empirical experiments of increasing complexity. Software with a graphic interface, freely available on a Web page, will allow the reader to replicate and vary (in simulation and on real robots) most of the experiments.
Jean-baptiste Mouret - One of the best experts on this subject based on the ideXlab platform.
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20 years of reality gap a few thoughts about simulators in Evolutionary Robotics
Genetic and Evolutionary Computation Conference, 2017Co-Authors: Jean-baptiste Mouret, Konstantinos ChatzilygeroudisAbstract:Simulators in Evolutionary Robotics (ER) are often considered as a "temporary evil" until experiments can be conducted on real robots. Yet, after more than 20 years of ER, most experiments still happen in simulation and nothing suggests that this situation will change in the next few years. In this short paper, we describe the requirements of ER from simulators, what we tried, and how we successfully crossed the "reality gap" in many experiments. We argue that future simulators need to be able to estimate their confidence when they predict a fitness value, so that behaviors that are not accurately simulated can be avoided.
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How to Promote Generalisation in Evolutionary Robotics: the ProGAb Approach
2016Co-Authors: Tony Pinville, Sylvain Koos, Jean-baptiste Mouret, Stephane DoncieuxAbstract:In Evolutionary Robotics (ER), controllers are assessed in a single or a few environments. As a consequence, good performances in new di erent contexts are not guaranteed. While a lot of ER works deal with robustness, i.e. the ability to perform well on new contexts close to the ones used for evaluation, no current approach is able to promote broader generalisation abilities without any assumption on the new contexts. In this paper, we introduce the ProGAb approach, which is based on the standard three data sets methodology of supervised machine learning, and compare it to state-of- the-art ER methods on two simulated robotic tasks: a navi- gation task in a T-maze and a more complex ball-collecting task in an arena. In both applications, the ProGAb ap- proach: (1) produced controllers with better generalisation abilities than the other methods; (2) needed two to three times fewer evaluations to discover such solutions.
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Evolutionary Robotics: what, why, and where to
Frontiers in Robotics and AI, 2015Co-Authors: Stephane Doncieux, Jean-baptiste Mouret, Nicolas Bredeche, Gusz EibenAbstract:Evolutionary Robotics applies the selection, variation, and heredity principles of natural evolution to the design of robots with embodied intelligence. It can be considered as a subfield of Robotics that aims to create more robust and adaptive robots. A pivotal feature of the Evolutionary approach is that it considers the whole robot at once, and enables the exploitation of robot features in a holistic manner. Evolutionary Robotics can also be seen as an innovative approach to the study of evolution based on a new kind of experimentalism. The use of robots as a substrate can help to address questions that are difficult, if not impossible, to investigate through computer simulations or biological studies. In this paper, we consider the main achievements of Evolutionary Robotics, focusing particularly on its contributions to both engineering and biology. We briefly elaborate on methodological issues, review some of the most interesting findings, and discuss important open issues and promising avenues for future work.
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Beyond black-box optimization: a review of selective pressures for Evolutionary Robotics
Evolutionary Intelligence, 2014Co-Authors: Stephane Doncieux, Jean-baptiste MouretAbstract:Evolutionary Robotics is often viewed as the application of a family of black-box optimization algorithms -- Evolutionary algorithms - - to the design of robots, or parts of robots. When considering Evolutionary Robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most Evolutionary Robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because Evolutionary Robotics experiments share common features, selective pressures for Evolutionary Robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.
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encouraging behavioral diversity in Evolutionary Robotics an empirical study
Evolutionary Computation, 2012Co-Authors: Jean-baptiste Mouret, Stephane DoncieuxAbstract:Evolutionary Robotics (ER) aims at automatically designing robots or controllers of robots without having to describe their inner workings. To reach this goal, ER researchers primarily employ phenotypes that can lead to an infinite number of robot behaviors and fitness functions that only reward the achievement of the task-and not how to achieve it. These choices make ER particularly prone to premature convergence. To tackle this problem, several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic Evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation; however, it also introduces new questions: how to compare behavior? should this comparison be task specific? and what is the best way to encourage diversity in this context? In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework. We compare each approach on three different tasks and two different genotypes. The results show that fostering behavioral diversity substantially improves the Evolutionary process in the investigated experiments, regardless of genotype or task. Among the benchmarked approaches, multi-objective methods were the most efficient and the generic, Hamming-based, behavioral distance was at least as efficient as task specific behavioral metrics.
Nicolas Bredeche - One of the best experts on this subject based on the ideXlab platform.
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behavioral specialization in embodied Evolutionary Robotics why so difficult
Frontiers in Robotics and AI, 2016Co-Authors: Jean-marc Montanier, Simon Carrignon, Nicolas BredecheAbstract:Embodied Evolutionary Robotics is an on-line distributed learning method used in collective Robotics where robots are facing open environments. This paper focuses on learning behavioural specialization, as defined by robots being able to demonstrate different kind of behaviours at the same time (e.g. division of labour). Using a foraging task with two resources available in limited quantities, we show that behavioural specialization is unlikely to evolve in the general case, unless very specific conditions are met regarding interactions between robots (a very sparse communication network is required) and the expected outcome of specialization (specialization into groups of similar sizes is easier to achieve). We also show that the population size (the larger the better) as well as the selection scheme used (favoring exploration over exploitation) both play important -- though not always mandatory -- roles. This research sheds light on why existing embodied evolution algorithms are limited with respect to learning efficient division of labour in the general case, ie. where it is not possible to guess before deployment if behavioural specialization is required or not, and gives directions to overcome current limitations.
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evolution of cooperation in Evolutionary Robotics the tradeoff between evolvability and efficiency
European Conference on Artificial Life, 2015Co-Authors: Arthur Bernard, Jean-baptiste André, Nicolas BredecheAbstract:In this paper, we investigate the benefits and drawbacks of different approaches for solving a cooperative foraging task with two robots. We compare a classical clonal approach with an additional approach which favors the evolution of heterogeneous behaviors according to two defining criteria: the evolvability of the cooperative solution and the efficiency of the coordination behaviors evolved. Our results reveal a tradeoff between evolvability and efficiency: the clonal approach evolves cooperation with a higher probability than a non-clonal approach, but heterogeneous behaviors evolved with the non-clonal approach systematically show better fitness scores. We then propose to overcome this tradeoff and improve on both of these criteria for each approach. To this end, we investigate the use of incremental evolution to transfer coordination behaviors evolved in a simpler task. We show that this leads to a significant increase in evolvability for the non-clonal approach, while the clonal approach does not benefit from any gain in terms of efficiency.
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Evolutionary Robotics: what, why, and where to
Frontiers in Robotics and AI, 2015Co-Authors: Stephane Doncieux, Jean-baptiste Mouret, Nicolas Bredeche, Gusz EibenAbstract:Evolutionary Robotics applies the selection, variation, and heredity principles of natural evolution to the design of robots with embodied intelligence. It can be considered as a subfield of Robotics that aims to create more robust and adaptive robots. A pivotal feature of the Evolutionary approach is that it considers the whole robot at once, and enables the exploitation of robot features in a holistic manner. Evolutionary Robotics can also be seen as an innovative approach to the study of evolution based on a new kind of experimentalism. The use of robots as a substrate can help to address questions that are difficult, if not impossible, to investigate through computer simulations or biological studies. In this paper, we consider the main achievements of Evolutionary Robotics, focusing particularly on its contributions to both engineering and biology. We briefly elaborate on methodological issues, review some of the most interesting findings, and discuss important open issues and promising avenues for future work.
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combining environment driven adaptation and task driven optimisation in Evolutionary Robotics
PLOS ONE, 2014Co-Authors: Evert Haasdijk, Nicolas Bredeche, Agoston E. EibenAbstract:Embodied Evolutionary Robotics is a sub-field of Evolutionary Robotics that employs Evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-)adjusting themselves to previously unknown or dynamically changing conditions autonomously, without human oversight. This paper addresses one of the major challenges that such systems face, viz. that the robots must satisfy two sets of requirements. Firstly, they must continue to operate reliably in their environment (viability), and secondly they must competently perform user-specified tasks (usefulness). The solution we propose exploits the fact that Evolutionary methods have two basic selection mechanisms–survivor selection and parent selection. This allows evolution to tackle the two sets of requirements separately: survivor selection is driven by the environment and parent selection is based on task-performance. This idea is elaborated in the Multi-Objective aNd open-Ended Evolution (monee) framework, which we experimentally validate. Experiments with robotic swarms of 100 simulated e-pucks show that monee does indeed promote task-driven behaviour without compromising environmental adaptation. We also investigate an extension of the parent selection process with a ‘market mechanism’ that can ensure equitable distribution of effort over multiple tasks, a particularly pressing issue if the environment promotes specialisation in single tasks.
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Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm
2011Co-Authors: Jean-marc Montanier, Nicolas BredecheAbstract:This paper deals with online onboard behavior optimization for a autonomous mobile robot in the scope of the European FP7 Symbrion Project. The work presented here extends the (1+1)-online algorithm introduced in [4]. The (1+1)-online algorithm has a limitation regarding the ability to perform global search whenever a local optimum is reached. Our new implementation of the algorithm, termed (1+1)-restart-online algorithm, addresses this issue and has been successfully experimented using a Cortex M3 microcontroller connected to a realistic robot simulator as well as within an autonomous robot based on an Atmel ATmega128 microcontroller. Results from the experiments show that the new algorithm is able to escape local optima and to perform behavior optimization in a complete autonomous fashion. As a consequence, it is able to converge faster and provides a richer set of relevant controllers compared to the previous implementation.
Dario Floreano - One of the best experts on this subject based on the ideXlab platform.
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evolution of adaptive behaviour in robots by means of darwinian selection
PLOS Biology, 2010Co-Authors: Dario Floreano, Laurent KellerAbstract:Keywords: Digital Organisms ; Swarm-Bot ; Cooperation ; Controllers ; Environments ; Locomotion ; Navigation ; Avoidance ; Sensors ; Walking ; Evolutionary Robotics Note: communication Reference LIS-ARTICLE-2010-003doi:10.1371/journal.pbio.1000292View record in Web of Science Record created on 2010-02-02, modified on 2017-05-10
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Evolutionary Robotics the next generation
Evolutionary Robotics III, 2000Co-Authors: Dario Floreano, J UrzelaiAbstract:After reviewing current approaches in Evolutionary Robotics, we point to directions of research that re likely to bring interesting results in the future. e then address two crucial aspects for future developments of Evolutionary Robotics: choice of fitness functions and scalability to real-world situations. In the first case we suggest framework to describe fitness functions, choose them according to the situation constraints, and compare available experiments in the literature on Evolutionary Robotics. In the second case, we suggest way to make experimental results applicable to real- world situations by evolving online continuous adaptive controllers. We also give an overview of recent experimental results showing that the suggested approaches pro- duce qualitatively superior abilities, scale up to more complex architectures, smoothly transfer from simulations to real robots and across different robotic platforms, and autonomously adapt in few seconds to several sources of strong variability that were not included during the Evolutionary run.
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Evolutionary Robotics the biology intelligence and technology of self organizing machines
2000Co-Authors: Stefano Nolfi, Dario FloreanoAbstract:Evolutionary Robotics is a new technique for the automatic creation of autonomous robots. Inspired by the Darwinian principle of selective reproduction of the fittest, it views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention. Drawing heavily on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic engineering. The resulting robots share with simple biological systems the characteristics of robustness, simplicity, small size, flexibility, and modularity. In Evolutionary Robotics, an initial population of artificial chromosomes, each encoding the control system of a robot, is randomly created and put into the environment. Each robot is then free to act (move, look around, manipulate) according to its genetically specified controller while its performance on various tasks is automatically evaluated. The fittest robots then "reproduce" by swapping parts of their genetic material with small random mutations. The process is repeated until the "birth" of a robot that satisfies the performance criteria. This book describes the basic concepts and methodologies of Evolutionary Robotics and the results achieved so far. An important feature is the clear presentation of a set of empirical experiments of increasing complexity. Software with a graphic interface, freely available on a Web page, will allow the reader to replicate and vary (in simulation and on real robots) most of the experiments.
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Evolutionary On-Line Self-Organization of Autonomous Robots
2000Co-Authors: Dario Floreano, Joseba UrzelaiAbstract:We review recent experiments in Evolutionary Robotics carried out in dynamic environments and across different robotic platforms. We then introduce a new Evolutionary approach where robots are evolved for their ability to adapt online. Several experiments show that this new approach is much faster, more powerful, and scalable than the traditional approach. 1 Evolutionary Robotics Autonomous robots are largely replacing computers as a metaphor for investigating natural and artificial intelligent systems because they interact with a real environment through sensors and actuators in a closed feedback loop, are subject to the laws of physics, operate in real-time, and are required to cope with partially unknown and unpredictable situations. Artificial evogenetic. method.eps 66 \Theta 41 mm ... Mutation Crossover Selective reproduction Evaluation Population manager Figure 1: A single physical robot is connected to a host computer through a serial cable with rotating contacts. The ..
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Competitive co-Evolutionary Robotics: from theory to practice
1998Co-Authors: Dario Floreano, Stefano Nolfi, Francesco MondadaAbstract:It is argued that competitive co-evolution is a viable methodology for developing truly autonomous and intelligent machines capable of setting their own goals in order to face new and continuously changing challenges. The paper starts giving an introduction to the dynamics of competitive co-Evolutionary systems and reviews their relevance from a computational perspective. The method is then applied to two mobile robots, a predator and a prey, which quickly and autonomously develop efficient chase and evasion strategies. The results are then explained and put in a long-term framework resorting to a visualization of the Red Queen effect on the fitness landscape. Finally, comparative data on different selection criteria are used to indicate that co-evolution does not optimize "intuitive" objective criteria.
