A Genetic Algorithm that Incorporates an Adaptive Mutation Based on an Evolutionary Model

Dealing with many free parameters and finding an appropriate set of parameter values for an evolutionary algorithm (EA) has been a longstanding major challenge of the Evolutionary Computation community. Such difficulty has directed researchers' attention towards devising an automated ways of controlling EA parameters. This work is concerned with proposing a novel method which adaptively adjusts EA and specifically genetic algorithm (GA) mutation rates. The proposed method incorporates the underlying statistical framework of biological evolutionary models into the generic context of evolutionary algorithms. By using such model, besides adapting the mutation rate, this method aims to wisely determine the types of replacing genes in the mutation procedure. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a wide array of test functions and the outcome is compared with a state-of-the-art adaptive mutation evolutionary algorithm. The results demonstrates that the newly suggested algorithm significantly outperform its adaptive rival in most of the test cases.

[1]  Inman Harvey,et al.  Error Thresholds and Their Relation to Optimal Mutation Rates , 2022 .

[2]  T. Jukes.  CHAPTER 24 – Evolution of Protein Molecules , 1969 .

[3]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

[4]  Christopher R. Stephens,et al.  Self-Adaptation in Evolving Systems , 1997, Artificial Life.

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

[6]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[7]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[8]  H. Kishino,et al.  Dating of the human-ape splitting by a molecular clock of mitochondrial DNA , 2005, Journal of Molecular Evolution.

[9]  S. Y. Yuen,et al.  A Genetic Algorithm That Adaptively Mutates and Never Revisits , 2009, IEEE Transactions on Evolutionary Computation.

[10]  Gabriela Ochoa,et al.  Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic , 2002, GECCO.

[11]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Eric R. Ziegel,et al.  Statistical Methods in Bioinformatics , 2002, Technometrics.

[13]  Thomas Bäck,et al.  Optimal Mutation Rates in Genetic Search , 1993, ICGA.

[14]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[15]  Thomas Bck,et al.  Self-adaptation in genetic algorithms , 1991 .

[16]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

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

[18]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[19]  Christopher R. Stephens,et al.  Limitations of Existing Mutation Rate Heuristics and How a Rank GA Overcomes Them , 2009, IEEE Transactions on Evolutionary Computation.

[20]  Christopher R. Stephens,et al.  "Optimal" mutation rates for genetic search , 2006, GECCO.

[21]  Jim Smith,et al.  Self adaptation of mutation rates in a steady state genetic algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[22]  Dirk Thierens,et al.  An Adaptive Pursuit Strategy for Allocating Operator Probabilities , 2005, BNAIC.

[23]  Weimin Xiao,et al.  Adaptively Evolving Probabilities of Genetic Operators , 2008, 2008 Seventh International Conference on Machine Learning and Applications.

[24]  S. Jeffery.  Evolution of Protein Molecules , 1979 .

[25]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[26]  Gabriela Ochoa,et al.  Error Thresholds in Genetic Algorithms , 2006, Evolutionary Computation.