The exploration/exploitation tradeoff in dynamic cellular genetic algorithms

This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in a methodological way by tuning the concept of ratio. Since the relationship (ratio) between the topology and the neighborhood shape defines the search selection pressure, we propose to analyze in depth the influence of this ratio on the exploration/exploitation tradeoff. As we will see, it is difficult to decide which ratio is best suited for a given problem. Therefore, we introduce a preprogrammed change of this ratio during the evolution as a possible additional improvement that removes the need of specifying a single ratio. A later refinement will lead us to the first adaptive dynamic kind of cellular models to our knowledge. We conclude that these dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy; we validate our results on a set of seven different problems of considerable complexity in order to better sustain our conclusions.

[1]  Kenneth A. De Jong,et al.  An Analysis of Local Selection Algorithms in a Spatially Structured Evolutionary Algorithm , 1997, ICGA.

[2]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[3]  Marco Tomassini,et al.  The Parallel Genetic Cellular Automata: Application to Global Function Optimization , 1993 .

[4]  Giandomenico Spezzano,et al.  Parallel hybrid method for SAT that couples genetic algorithms and local search , 2001, IEEE Trans. Evol. Comput..

[5]  L. Darrell Whitley,et al.  Evaluating Evolutionary Algorithms , 1996, Artif. Intell..

[6]  Francisco Herrera,et al.  Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[7]  Enrique Alba,et al.  Selection Intensity in Asynchronous Cellular Evolutionary Algorithms , 2003, GECCO.

[8]  Wan-Chi Siu,et al.  Adding learning to cellular genetic algorithms for training recurrent neural networks , 1999, IEEE Trans. Neural Networks.

[9]  Shumeet Baluja,et al.  Structure and Performance of Fine-Grain Parallelism in Genetic Search , 1993, ICGA.

[10]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[11]  Markus Schwehm,et al.  Massively Parallel Genetic Algorithms , 1994, EUROSIM.

[12]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[13]  Vassilios Petridis,et al.  Co-operating Populations with Different Evolution Behaviours , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[14]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[15]  Heinz Mühlenbein,et al.  Adaptation of population sizes by competing subpopulations , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[16]  Jim Smith,et al.  Replacement Strategies in Steady State Genetic Algorithms: Static Environments , 1998, FOGA.

[17]  Zbigniew Michalewicz,et al.  Adaptation in evolutionary computation: a survey , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[18]  Peter J. Angeline,et al.  Adaptive and Self-adaptive Evolutionary Computations , 1995 .

[19]  Lawrence Davis,et al.  Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

[20]  Hao Chen,et al.  Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm , 1998, IEEE Trans. Parallel Distributed Syst..

[21]  O. Antoine,et al.  Theory of Error-correcting Codes , 2022 .

[22]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[23]  Enrique Alba,et al.  Improving flexibility and efficiency by adding parallelism to genetic algorithms , 2002, Stat. Comput..

[24]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[25]  Thomas Bck Introduction to the Special Issue: Self-Adaptation , 2001, Evolutionary Computation.

[26]  I. Wegener,et al.  Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods A Natural and Simple Function Which is Hard For All Evolutionary Algorithms , 2007 .

[27]  Anany Levitin,et al.  Introduction to the Design and Analysis of Algorithms , 2002 .

[28]  Enrique Alba,et al.  Cellular Evolutionary Algorithms: Evaluating the Influence of Ratio , 2000, PPSN.

[29]  Qingfu Zhang,et al.  On the convergence of a class of estimation of distribution algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[30]  L. Darrell Whitley,et al.  Cellular Genetic Algorithms , 1993, ICGA.

[31]  Kenneth A. De Jong,et al.  An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms , 1996, PPSN.

[32]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

[33]  Shigeyoshi Tsutsui,et al.  Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA) , 1993, ICGA.

[34]  Thomas Bäck,et al.  An evolutionary approach to combinatorial optimization problems , 1994, CSC '94.

[35]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[36]  Kenneth A. De Jong,et al.  Using Problem Generators to Explore the Effects of Epistasis , 1997, ICGA.