Variation Operator

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Roberto Limao - One of the best experts on this subject based on the ideXlab platform.

Peter A N Bosman - One of the best experts on this subject based on the ideXlab platform.

  • improving the performance of mo rv gomea on problems with many objectives using tchebycheff scalarizations
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Ngoc Hoang Luong, Tanja Alderliesten, Peter A N Bosman
    Abstract:

    The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been shown to exhibit excellent performance in solving various bi-objective benchmark and real-world problems. We assess the competence of MO-RV-GOMEA in tackling many-objective problems, which are normally defined as problems with at least four conflicting objectives. Most Pareto dominance-based Multi-Objective Evolutionary Algorithms (MOEAs) typically diminish in performance if the number of objectives is more than three because selection pressure toward the Pareto-optimal front is lost. This is potentially less of an issue for MO-RV-GOMEA because its Variation Operator creates each offspring solution by iteratively altering a currently existing solution in a few decision variables each time, and changes are only accepted if they result in a Pareto improvement. For most MOEAs, integrating scalarization methods is potentially beneficial in the many-objective context. Here, we investigate the possibility of improving the performance of MO-RV-GOMEA by further guiding improvement checks during solution Variation in MO-RV-GOMEA with carefully constructed Tchebycheff scalarizations. Results obtained from experiments performed on a selection of well-known problems from the DTLZ and WFG test suites show that MO-RV-GOMEA is by design already well-suited for many-objective problems. Moreover, by enhancing it with Tchebycheff scalarizations, it outperforms M0EA/D-2TCHMFI, a state-of-the-art decomposition-based MOEA.

  • application and benchmarking of multi objective evolutionary algorithms on high dose rate brachytherapy planning for prostate cancer treatment
    Swarm and evolutionary computation, 2017
    Co-Authors: Ngoc Hoang Luong, Tanja Alderliesten, A Bel, Yury Niatsetski, Peter A N Bosman
    Abstract:

    Abstract High-Dose-Rate (HDR) brachytherapy (BT) treatment planning involves determining an appropriate schedule of a radiation source moving through a patient's body such that target volumes are irradiated with the planning-aim dose as much as possible while healthy tissues (i.e., organs at risk) should not be irradiated more than certain thresholds. Such movement of a radiation source can be defined by so-called dwell times at hundreds of potential dwell positions, which must be configured to satisfy a clinical protocol of multiple different treatment criteria within a strictly-limited time frame of not more than one hour. In this article, we propose a bi-objective optimization model that intuitively encapsulates in two objectives the complicated high-dimensional multi-criteria nature of the BT treatment planning problem. The resulting Pareto-optimal fronts exhibit possible trade-offs between the coverage of target volumes and the sparing of organs at risk, thereby intuitively facilitating the decision-making process of treatment planners when creating a clinically-acceptable plan. We employ real medical data for conducting experiments and benchmark four different Multi-Objective Evolutionary Algorithms (MOEAs) on solving our problem: the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), the Multi-objective Adapted Maximum-Likelihood Gaussian Model Iterated Density-Estimation Evolutionary Algorithm (MAMaLGaM), and the recently-introduced Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA). The Variation Operator that is specific to MO-RV-GOMEA enables performing partial evaluations to efficiently calculate objective values of offspring solutions without incurring the cost of fully recomputing the radiation dose distributions for new treatment plans. Experimental results show that MO-RV-GOMEA is the best performing MOEA that effectively exploits dependencies between decision variables to efficiently solve the multi-objective BT treatment planning problem.

  • exploiting linkage information in real valued optimization with the real valued gene pool optimal mixing evolutionary algorithm
    Genetic and Evolutionary Computation Conference, 2017
    Co-Authors: Anton Bouter, Tanja Alderliesten, Cees Witteveen, Peter A N Bosman
    Abstract:

    The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) has been shown to be among the state-of-the-art for solving discrete optimization problems. Key to the success of GOMEA is its ability to efficiently exploit the linkage structure of a problem. Here, we introduce the Real-Valued GOMEA (RV-GOMEA), which incorporates several aspects of the real-valued EDA known as AMaLGaM into GOMEA in order to make GOMEA well-suited for real-valued optimization. The key strength of GOMEA to competently exploit linkage structure is effectively preserved in RV-GOMEA, enabling excellent performance on problems that exhibit a linkage structure that is to some degree decomposable. Moreover, the main Variation Operator of GOMEA enables substantial improvements in performance if the problem allows for partial evaluations, which may be very well possible in many real-world applications. Comparisons of performance with state-of-the-art algorithms such as CMA-ES and AMaLGaM on a set of well-known benchmark problems show that RV-GOMEA achieves comparable, excellent scalability in case of black-box optimization. Moreover, RV-GOMEA achieves unprecedented scalability on problems that allow for partial evaluations, reaching near-optimal solutions for problems with up to millions of real-valued variables within one hour on a normal desktop computer.

  • scalable genetic programming by gene pool optimal mixing and input space entropy based building block learning
    Genetic and Evolutionary Computation Conference, 2017
    Co-Authors: Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A N Bosman
    Abstract:

    The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recently introduced model-based EA that has been shown to be capable of outperforming state-of-the-art alternative EAs in terms of scalability when solving discrete optimization problems. One of the key aspects of GOMEA's success is a Variation Operator that is designed to extensively exploit linkage models by effectively combining partial solutions. Here, we bring the strengths of GOMEA to Genetic Programming (GP), introducing GP-GOMEA. Under the hypothesis of having little problem-specific knowledge, and in an effort to design easy-to-use EAs, GP-GOMEA requires no parameter specification. On a set of well-known benchmark problems we find that GP-GOMEA outperforms standard GP while being on par with more recently introduced, state-of-the-art EAs. We furthermore introduce Input-space Entropy-based Building-block Learning (IEBL), a novel approach to identifying and encapsulating relevant building blocks (subroutines) into new terminals and functions. On problems with an inherent degree of modularity, IEBL can contribute to compact solution representations, providing a large potential for knock-on effects in performance. On the difficult, but highly modular Even Parity problem, GP-GOMEA+IEBL obtains excellent scalability, solving the 14-bit instance in less than 1 hour.

Michael Kirley - One of the best experts on this subject based on the ideXlab platform.

  • the pareto following Variation Operator as an alternative approximation model
    Congress on Evolutionary Computation, 2009
    Co-Authors: A Khaled Ahsan K M Talukder, Michael Kirley, Rajkumar Buyya
    Abstract:

    This paper presents a critical analysis of the Pareto-Following Variation Operator (PFVO) when used as an approximation method for Multiobjective Evolutionary Algorithms (MOEA). In previous work, we have described the development and implementation of the PFVO. The simulation results reported indicated that when the PFVO was integrated with NSGA-II there was a significant increase in the convergence speed of the algorithm. In this study, we extend this work. We claim that when the PFVO is combined with any MOEA that uses a non-dominated sorting routine before selection, it will lead to faster convergence and high quality solutions. Numerical results are presented for two base algorithms: SPEA-II and RM-MEDA to support are claim. We also describe enhancements to the approximation method that were introduced so that the enhanced algorithm was able to track the Pareto-optimal front in the right direction.

  • IEEE Congress on Evolutionary Computation - The Pareto-Following Variation Operator as an alternative approximation model
    2009 IEEE Congress on Evolutionary Computation, 2009
    Co-Authors: Akm Khaled Ahsan Talukder, Michael Kirley, Rajkumar Buyya
    Abstract:

    This paper presents a critical analysis of the Pareto-Following Variation Operator (PFVO) when used as an approximation method for Multiobjective Evolutionary Algorithms (MOEA). In previous work, we have described the development and implementation of the PFVO. The simulation results reported indicated that when the PFVO was integrated with NSGA-II there was a significant increase in the convergence speed of the algorithm. In this study, we extend this work. We claim that when the PFVO is combined with any MOEA that uses a non-dominated sorting routine before selection, it will lead to faster convergence and high quality solutions. Numerical results are presented for two base algorithms: SPEA-II and RM-MEDA to support are claim. We also describe enhancements to the approximation method that were introduced so that the enhanced algorithm was able to track the Pareto-optimal front in the right direction.

  • a pareto following Variation Operator for fast converging multiobjective evolutionary algorithms
    Genetic and Evolutionary Computation Conference, 2008
    Co-Authors: A Khaled Ahsan K M Talukder, Michael Kirley, Rajkumar Buyya
    Abstract:

    One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate (or surrogate) methods offer the possibility of reducing the number of evaluations, without reducing solution quality. Artificial Neural Network (ANN) based models are one approach that have been used to approximate the future front from the current available fronts with acceptable accuracy levels. However, the associated computational costs limit their effectiveness. In this work, we introduce a simple approach that has comparatively smaller computational cost and we have developed this model as a Variation Operator that can be used in any kind of multiobjective optimizer. When designing this model, we have considered the whole search procedure as a dynamic system that takes available objective values in current front as input and generates approximated design variables for the next front as output. Initial simulation experiments have produced encouraging results in comparison to NSGA-II. Our motivation was to increase the speed of the hosting optimizer. We have compared the performance of the algorithm with respect to the total number of function evaluation and Hypervolume metric. This Variation Operator has worst case complexity of O(nkN3), where N is the population size, n and k is the number of design variables and objectives respectively.

  • a pareto following Variation Operator for evolutionary dynamic multi objective optimization
    World Congress on Computational Intelligence, 2008
    Co-Authors: A K M Khaled, Akm Khaled Ahsan Talukder, Michael Kirley
    Abstract:

    Tracking the Pareto-front in a dynamic multi-objective optimization problem (MOP) is a challenging task. Evolutionary algorithms are a representative meta-heuristic capable of meeting this challenge. Typically, the stochastic Variation Operators used in an evolutionary algorithm work in decision (or design) variable space, thus there are no guarantees that the new individuals produced are non-dominated and/or are unique in the population. In this paper, we introduce a novel Variation Operator that manipulates the values in both objective space and design variable space in such a way that it can avoid re-exploration of dominated solutions. The proposed Operator, inspired by the theory of dynamic system identification, is based on integral transformation. Here, we approximate the next expected Pareto-front, and from this expected front, we generate corresponding correct decision variables. We show empirically that our algorithm can approximate the Pareto-optimal set for given static benchmark MOPpsilas and that it can track changes in the Pareto-front for particular dynamic MOPpsilas.

  • GECCO - A pareto following Variation Operator for fast-converging multiobjective evolutionary algorithms
    Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08, 2008
    Co-Authors: A Khaled Ahsan K M Talukder, Michael Kirley, Rajkumar Buyya
    Abstract:

    One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate (or surrogate) methods offer the possibility of reducing the number of evaluations, without reducing solution quality. Artificial Neural Network (ANN) based models are one approach that have been used to approximate the future front from the current available fronts with acceptable accuracy levels. However, the associated computational costs limit their effectiveness. In this work, we introduce a simple approach that has comparatively smaller computational cost and we have developed this model as a Variation Operator that can be used in any kind of multiobjective optimizer. When designing this model, we have considered the whole search procedure as a dynamic system that takes available objective values in current front as input and generates approximated design variables for the next front as output. Initial simulation experiments have produced encouraging results in comparison to NSGA-II. Our motivation was to increase the speed of the hosting optimizer. We have compared the performance of the algorithm with respect to the total number of function evaluation and Hypervolume metric. This Variation Operator has worst case complexity of O(nkN3), where N is the population size, n and k is the number of design variables and objectives respectively.

Ricardo Landa-becerra - One of the best experts on this subject based on the ideXlab platform.

  • Artificial Evolution - A surrogate-based intelligent Variation Operator for multiobjective optimization
    Lecture Notes in Computer Science, 2012
    Co-Authors: Alan Diaz-manriquez, Gregorio Toscano-pulido, Ricardo Landa-becerra
    Abstract:

    Evolutionary algorithms are meta-heuristics that have shown flexibility, adaptability and good performance when solving Multiobjective Optimization Problems (MOPs). However, in order to achieve acceptable results, Multiobjective Evolutionary Algorithms (MOEAs) usually require several evaluations of the optimization function. Moreover, when each of these evaluations represents a high computational cost, these expensive problems remain intractable even by these meta-heuristics. To reduce the computational cost in expensive optimization problems, some researchers have replaced the real optimization function with a computationally inexpensive surrogate model. In this paper, we propose a new intelligent Variation Operator which is based on surrogate models. The Operator is incorporated into a stand-alone search mechanism in order to perform its validation. Results indicate that the proposed algorithm can be used to optimize MOPs. However, it presents premature convergence when optimizing multifrontal MOPs. Therefore, in order to solve this drawback, the proposed Operator was successfully hybridized with a MOEA. Results show that this latter approach outperformed both, the former proposed algorithm and the evolutionary algorithm but without the Operator.

Mario Augusto Da Costa Torres - One of the best experts on this subject based on the ideXlab platform.

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