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Xin Yao - One of the best experts on this subject based on the ideXlab platform.

  • many objective Evolutionary Algorithms a survey
    ACM Computing Surveys, 2015
    Co-Authors: Ke Tang, Xin Yao
    Abstract:

    Multiobjective Evolutionary Algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective Evolutionary Algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.

  • a study of drift analysis for estimating computation time of Evolutionary Algorithms
    Natural Computing, 2004
    Co-Authors: Xin Yao
    Abstract:

    This paper introduces drift analysis and its applications in estimating average computation time of Evolutionary Algorithms. Firstly, drift conditions for estimating upper and lower bounds of the mean first hitting times of Evolutionary Algorithms are presented. Then drift analysis is applied to two specific Evolutionary Algorithms and problems. Finally, a general classification of easy and hard problems for Evolutionary Algorithms is given based on the analysis.

  • towards an analytic framework for analysing the computation time of Evolutionary Algorithms
    Artificial Intelligence, 2003
    Co-Authors: Xin Yao
    Abstract:

    In spite of many applications of Evolutionary Algorithms in optimisation, theoretical results on the computation time and time complexity of Evolutionary Algorithms on different optimisation problems are relatively few. It is still unclear when an Evolutionary algorithm is expected to solve an optimisation problem efficiently or otherwise. This paper gives a general analytic framework for analysing first hitting times of Evolutionary Algorithms. The framework is built on the absorbing Markov chain model of Evolutionary Algorithms. The first step towards a systematic comparative study among different EAs and their first hitting times has been made in the paper.

  • Fast Evolutionary Algorithms
    Natural Computing Series, 2003
    Co-Authors: Xin Yao, Yong Liu, Ko-hsin Liang, Guangming Lin
    Abstract:

    This chapter discusses a number of recent results in Evolutionary optimization. In particular, we show that the search step size of a variation operator plays a vital role in its efficient search of a landscape. We have derived the optimal search step size of mutation operators in Evolutionary optimization. Based on this theoretical analysis, we have developed several new Evolutionary Algorithms which outperform existing Evolutionary Algorithms significantly on many benchmark functions.Most of the existing work in Evolutionary optimization concentrates on different variation (i.e., search) operators, such as crossover and mutation. However, there may be a better way to solve a complex problem by transforming it into a simpler one first and then solving it. The key issue here is how to approximate the problem without changing the nature of the problem (i.e., the optima we wish to find). This chapter will present the latest results on landscape approximation and hybrid Evolutionary Algorithms.

  • Towards an analytic framework for analysing the computation time of Evolutionary Algorithms
    Artificial Intelligence, 2003
    Co-Authors: Xin Yao
    Abstract:

    He, J., Yao, X. (2003). Towards an Analytic Framework for Analysing the Computation Time of Evolutionary Algorithms. Artificial Intelligence, 145 (1-2), 59-97 RAE2008In spite of many applications of Evolutionary Algorithms in optimisation, theoretical results on the computation time and time complexity of Evolutionary Algorithms on different optimisation problems are relatively few. It is still unclear when an Evolutionary algorithm is expected to solve an optimisation problem efficiently or otherwise. This paper gives a general analytic framework for analysing first hitting times of Evolutionary Algorithms. The framework is built on the absorbing Markov chain model of Evolutionary Algorithms. The first step towards a systematic comparative study among different EAs and their first hitting times has been made in the paper.Peer reviewe

Mihai Oltean - One of the best experts on this subject based on the ideXlab platform.

  • Evolutionary design of Evolutionary Algorithms
    Genetic Programming and Evolvable Machines, 2009
    Co-Authors: Laura Diosan, Mihai Oltean
    Abstract:

    Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct Algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by Evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

  • Evolving Evolutionary Algorithms with Patterns
    Soft Computing, 2007
    Co-Authors: Mihai Oltean
    Abstract:

    A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the multi expression programming (MEP) technique. Each MEP chromosome encodes an Evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard Evolutionary scheme which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are evolved by using the considered model. The evolved Evolutionary Algorithms are compared with a human-designed genetic algorithm. Numerical experiments show that the evolved Evolutionary Algorithms can compete with standard approaches for several well-known benchmarking problems.

  • GECCO (Companion) - Evolving Evolutionary Algorithms using Evolutionary Algorithms
    Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation - GECCO '07, 2007
    Co-Authors: Laura Diosan, Mihai Oltean
    Abstract:

    A new model for automatic generation of Evolutionary Algorithms (EAs) by Evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

  • evolving Evolutionary Algorithms using linear genetic programming
    Evolutionary Computation, 2005
    Co-Authors: Mihai Oltean
    Abstract:

    A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

Marjan Mernik - One of the best experts on this subject based on the ideXlab platform.

  • a chess rating system for Evolutionary Algorithms a new method for the comparison and ranking of Evolutionary Algorithms
    Information Sciences, 2014
    Co-Authors: Niki Vecek, Marjan Mernik, Matej Crepinsek
    Abstract:

    Abstract The Null Hypothesis Significance Testing (NHST) is of utmost importance for comparing Evolutionary Algorithms as the performance of one algorithm over another can be scientifically proven. However, NHST is often misused, improperly applied and misinterpreted. In order to avoid the pitfalls of NHST usage this paper proposes a new method, a Chess Rating System for Evolutionary Algorithms (CRS4EAs) for the comparison and ranking of Evolutionary Algorithms. A computational experiment in CRS4EAs is conducted in the form of a tournament where the Evolutionary Algorithms are treated as chess players and a comparison between the solutions of two Algorithms on the objective function is treated as one game outcome. The rating system used in CRS4EAs was inspired by the Glicko-2 rating system, based on the Bradley–Terry model for dynamic pairwise comparisons, where each algorithm is represented by rating, rating deviation, a rating/confidence interval, and rating volatility. The CRS4EAs was empirically compared to NHST within a computational experiment conducted on 16 Evolutionary Algorithms and a benchmark suite of 20 numerical minimisation problems. The analysis of the results shows that the CRS4EAs is comparable with NHST but may also have many additional benefits. The computations in CRS4EAs are less complicated and sensitive than those in statistical significance tests, the method is less sensitive to outliers, reliable ratings can be obtained over a small number of runs, and the conservativity/liberality of CRS4EAs is easier to control.

  • a chess rating system for Evolutionary Algorithms a new method for the comparison and ranking of Evolutionary Algorithms
    Information Sciences, 2014
    Co-Authors: Niki Vecek, Marjan Mernik, Matej Crepinsek
    Abstract:

    Abstract The Null Hypothesis Significance Testing (NHST) is of utmost importance for comparing Evolutionary Algorithms as the performance of one algorithm over another can be scientifically proven. However, NHST is often misused, improperly applied and misinterpreted. In order to avoid the pitfalls of NHST usage this paper proposes a new method, a Chess Rating System for Evolutionary Algorithms (CRS4EAs) for the comparison and ranking of Evolutionary Algorithms. A computational experiment in CRS4EAs is conducted in the form of a tournament where the Evolutionary Algorithms are treated as chess players and a comparison between the solutions of two Algorithms on the objective function is treated as one game outcome. The rating system used in CRS4EAs was inspired by the Glicko-2 rating system, based on the Bradley–Terry model for dynamic pairwise comparisons, where each algorithm is represented by rating, rating deviation, a rating/confidence interval, and rating volatility. The CRS4EAs was empirically compared to NHST within a computational experiment conducted on 16 Evolutionary Algorithms and a benchmark suite of 20 numerical minimisation problems. The analysis of the results shows that the CRS4EAs is comparable with NHST but may also have many additional benefits. The computations in CRS4EAs are less complicated and sensitive than those in statistical significance tests, the method is less sensitive to outliers, reliable ratings can be obtained over a small number of runs, and the conservativity/liberality of CRS4EAs is easier to control.

  • Hybridization of Evolutionary Algorithms
    Evolutionary Algorithms, 2011
    Co-Authors: Iztok Fister, Marjan Mernik, Janez Brest
    Abstract:

    Evolutionary Algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to Evolutionary Algorithms by hybridizing. Interestingly, all the elements of the Evolutionary Algorithms can be hybridized. In this chapter, the hybridization of the three elements of the Evolutionary Algorithms is discussed: the objective function, the survivor selection operator and the parameter settings. As an objective function, the existing heuristic function that construct the solution of the problem in traditional way is used. However, this function is embedded into the Evolutionary algorithm that serves as a generator of new solutions. In addition, the objective function is improved by local search heuristics. The new neutral selection operator has been developed that is capable to deal with neutral solutions, i.e. solutions that have the different representation but expose the equal values of objective function. The aim of this operator is to directs the Evolutionary search into a new undiscovered regions of the search space. To avoid of wrong setting of parameters that control the behavior of the Evolutionary algorithm, the self-adaptation is used. Finally, such hybrid self-adaptive Evolutionary algorithm is applied to the two real-world NP-hard problems: the graph 3-coloring and the optimization of markers in the clothing industry. Extensive experiments shown that these hybridization improves the results of the Evolutionary Algorithms a lot. Furthermore, the impact of the particular hybridizations is analyzed in details as well.

Thomas Hanne - One of the best experts on this subject based on the ideXlab platform.

  • On the convergence of multiobjective Evolutionary Algorithms
    European Journal of Operational Research, 1999
    Co-Authors: Thomas Hanne
    Abstract:

    We consider the usage of Evolutionary Algorithms for multiobjective programming (MOP), i.e. for decision problems with alternatives taken from a real-valued vector space and evaluated according to a vector-valued objective function. Selection mechanisms, possibilities of temporary fitness deterioration, and problems of unreachable alternatives for such multiobjective Evolutionary Algorithms (MOEAs) are studied. Theoretical properties of MOEAs such as stochastic convergence with probability 1 are analyzed.

Matej Crepinsek - One of the best experts on this subject based on the ideXlab platform.

  • a chess rating system for Evolutionary Algorithms a new method for the comparison and ranking of Evolutionary Algorithms
    Information Sciences, 2014
    Co-Authors: Niki Vecek, Marjan Mernik, Matej Crepinsek
    Abstract:

    Abstract The Null Hypothesis Significance Testing (NHST) is of utmost importance for comparing Evolutionary Algorithms as the performance of one algorithm over another can be scientifically proven. However, NHST is often misused, improperly applied and misinterpreted. In order to avoid the pitfalls of NHST usage this paper proposes a new method, a Chess Rating System for Evolutionary Algorithms (CRS4EAs) for the comparison and ranking of Evolutionary Algorithms. A computational experiment in CRS4EAs is conducted in the form of a tournament where the Evolutionary Algorithms are treated as chess players and a comparison between the solutions of two Algorithms on the objective function is treated as one game outcome. The rating system used in CRS4EAs was inspired by the Glicko-2 rating system, based on the Bradley–Terry model for dynamic pairwise comparisons, where each algorithm is represented by rating, rating deviation, a rating/confidence interval, and rating volatility. The CRS4EAs was empirically compared to NHST within a computational experiment conducted on 16 Evolutionary Algorithms and a benchmark suite of 20 numerical minimisation problems. The analysis of the results shows that the CRS4EAs is comparable with NHST but may also have many additional benefits. The computations in CRS4EAs are less complicated and sensitive than those in statistical significance tests, the method is less sensitive to outliers, reliable ratings can be obtained over a small number of runs, and the conservativity/liberality of CRS4EAs is easier to control.

  • a chess rating system for Evolutionary Algorithms a new method for the comparison and ranking of Evolutionary Algorithms
    Information Sciences, 2014
    Co-Authors: Niki Vecek, Marjan Mernik, Matej Crepinsek
    Abstract:

    Abstract The Null Hypothesis Significance Testing (NHST) is of utmost importance for comparing Evolutionary Algorithms as the performance of one algorithm over another can be scientifically proven. However, NHST is often misused, improperly applied and misinterpreted. In order to avoid the pitfalls of NHST usage this paper proposes a new method, a Chess Rating System for Evolutionary Algorithms (CRS4EAs) for the comparison and ranking of Evolutionary Algorithms. A computational experiment in CRS4EAs is conducted in the form of a tournament where the Evolutionary Algorithms are treated as chess players and a comparison between the solutions of two Algorithms on the objective function is treated as one game outcome. The rating system used in CRS4EAs was inspired by the Glicko-2 rating system, based on the Bradley–Terry model for dynamic pairwise comparisons, where each algorithm is represented by rating, rating deviation, a rating/confidence interval, and rating volatility. The CRS4EAs was empirically compared to NHST within a computational experiment conducted on 16 Evolutionary Algorithms and a benchmark suite of 20 numerical minimisation problems. The analysis of the results shows that the CRS4EAs is comparable with NHST but may also have many additional benefits. The computations in CRS4EAs are less complicated and sensitive than those in statistical significance tests, the method is less sensitive to outliers, reliable ratings can be obtained over a small number of runs, and the conservativity/liberality of CRS4EAs is easier to control.