Summary
For successful and efficient use of GAs, it is not enough to simply apply simple GAs (SGAs). In addition, it is necessary to find a proper representation for the problem and to integrate linkage information about the problem structure. Similarly, it is important to develop appropriate search operators that fit well to the properties of the genotype encoding and that can learn linkage information to assisst in creating and not in destroying the building blocks. Besides, the representation must at least be able to encode all possible solutions of an optimization problem, and genetic operators such as crossover and mutation should be applicable to it. In this chapter, sequential alternation strategies between two coding schemes are formulated in the framework of dynamic change of genotype encoding in GAs for function optimization. Likewise, new variants of GAs for difficult optimization problems are developed using a parallel implementation of GAs and evolving a dynamic exchange of individual representation in the context of dual coding concepts. Numerical experiments show that the evolved proposals significantly outperform a SGA with static single coding.
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References
Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1975)
Gregory, J.E., Rawlins.: Foundations of Genetic Algorithms - 1. Morgan Kaufman Publishers, San Mateo (1991)
Rothlauf, F., Goldberg, D.E.: Representations for Genetic and Evolutionary Algorithms. Springer, New York (2002)
Digalakis, J.G., Margaritis, K.G.: An Experimental Study of Benchmarking Functions for Genetic Algorithms (2002)
Crutchfield, J.P., Schuster, P.: Evolutionary Dynamics, Exploring the Interplay of Selection, Accident, Neutrality and Function. Oxford University Press, New York (2003)
Caruana, Rich, Schaffer, David, J.: Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. In: Proceedings of the Fifth International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco (1988)
Mathias, E., Whitley, D.: Transforming the Search Space with Gray Coding. In: Proceedings of the 1994 International Conference on Evolutionary Computation (1994)
Whitley, D.: A Free Lunch Proof for Gray versus Binary Encodings. In: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann Publishers, Orlando (1999)
Whitley, D., Rana, S., Heckendorn, R.B.: Representation Issues in Neighborhood Search and Evolutionary Algorithms. In: Genetic Algorithms in Engineering and Computer Science (1997)
Barbulescu, L., Watson, J.-P., Whitley, D.: Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search. In: AAAI/IAAI (2000)
Toussaint, M.: Compact Representations as a Search Strategy: Compression EDAs. Elsevier Science Publishers Ltd, Essex (2006)
Rothlauf, F., Goldberg, D.E., Heinzl, A.: Network Random Keys: a Tree Representations Scheme for Genetic and Evolutionary Algorithms. MIT Press, Cambridge (2002)
Liepins, G., Vose, M.: Representations Issues in Genetic Algorithms, Experimental and Theoretical Artificial Intelligence Journal (1990)
Eshelman, L.J.: The CHC Adapative Search Algorithm: How to Have Safe Search when Engaging in Non-Traditional Genetic Recombination. In: Foundations of Genetic Algorithms - 1. Morgan Kaufmann, San Francisco (1991)
De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. dissertation, University of Michigan (1975)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Trkmen, B.S., Turan, O.: An Application Study of Multi-Agent Systems in Multi-criterion Ship Design Optimisation. In: Proceedings of the Third International EuroConference on Computer and IT Applications in the Maritime Industries (COMPIT 2004), Siguenza, Madrid (2004)
Whitley, D., Rana. S.: Representation, Search and Genetic Algorithm. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI 1997). AAAI Press/MIT Press (1997)
Eshelman, L.J., Mathias, K.E., Schaffer, J.D.: Convergence Controlled Variation. In: Foundations of Genetic Algorithms - 4. Morgan Kaufmann, San Francisco (1997)
Tsutsui, S., Fujimoto, Y.: Forking Genetic Algorithm with Blocking and Shrinking Modes. In: Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco (1993)
Ackley, D.: An Empirical Study of Bit Vector Function Optimization’, Genetic Algorithms and Simulated Annealing (1987)
Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part I, a Gentle Introduction. Complex Systems (1989)
Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part II, Deception and its Analysis. Complex Systems (1989)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Co, Reading (1989)
Baluja, S.: Population-based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, Tech. Report No. CMU-CS-94-163, Pittsburgh, PA: Carnegie Mellon University (1994)
Baluja, S., Davies, S.: Using Optimal Dependency-trees for Combinatorial Optimization: Learning the Structure of the Search Space. In: Proceedings of the Fourteenth International Conference on Machine Learning (1997)
Muhlenbein, H., Paa, G.: From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. In: Proceedings of the Fourth International Conference on Parallel Problem Solving from Nature (1996)
Muhlenbein, H.: The Equation for Response to Selection and its Use for Prediction. Evolutionary Computation Journal (1997)
Muhlenbein, H., Mahnig, T.: Convergence Theory and Applications of the Factorized Distribution Algorithm. Computing and Information Technology Journal (1999)
Muhlenbein, H., Mahnig, T.: A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions. Evolutionary Computation Journal (1999)
Muhlenbein, H., Mahnig, T., Ochoa, A.: Schemata, Distributions and Graphical Models in Evolutionary Optimization. Heuristics Journal (1999)
Kargupta, H.: The Gene Expression Messy Genetic Algorithm. In: Proceedings of IEEE International Conference on Evolutionary Computation (1996)
Bandyopadhyay, S., Kargupta, H., Wang, G.: Revisiting the GEMGA: Scalable Evolutionary Optimization through Linkage Learning. In: Proceedings of IEEE International Conference on Evolutionary Computation (1998)
Bosman, P.A.N., Thierens, D.: Linkage Information Processing in Distribution Estimation Algorithms. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 1999) (1999)
Harik, G.: Learning Gene Linkage to Efficiently Solve Problems of Bounded Difficulty Using Genetic Algorithms, Ph.D. dissertation, University of Michigan (1997)
Harik, G.: Linkage Learning via Probabilistic Modeling in the ECGA, IlliGAL Report No. 99010 (1999)
Chen, Y.-p., Yu, T.-L., Sastry, K., Goldberg, D.E.: A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms, IlliGAL Report No. 2007014 (2007)
Heckendorn, R.B., Wright, A.H.: Efficient Linkage Discovery by Limited Probing. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2003) (2003)
Munetomo, M., Goldberg, D.E.: Identifying Linkage Groups by Non-linearity/Non-monotonicity Detection. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 1999) (1999)
Munetomo, M., Goldberg, D.E.: Linkage Identification by Non-monotonicity Detection for Overlapping Functions. Evolutionary Computation Journal (1999)
Pelikan, M., Goldberg, D.E., Cantu-Paz, E.: Linkage Problem, Distribution Estimation, and Bayesian Networks. Evolutionary Computation Journal (2000)
Bercachi, M., Collard, P., Clergue, M., Verel, S.: Evolving Dynamic Change and Exchange of Genotype Encoding in Genetic Algorithms for Difficult Optimization Problems. In: Proceedings of IEEE International Congress on Evolutionary Computation CEC 2007 (2007)
Collard, P., Aurand, J.-P.: DGA: an efficient Genetic Algorithm. In: Proceedings of ECAI 1994: 11th European Conference on Artificial Intelligence (1994)
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Bercachi, M., Collard, P., Clergue, M., Verel, S. (2008). Studying the Effects of Dual Coding on the Adaptation of Representation for Linkage in Evolutionary Algorithms. In: Chen, Yp., Lim, MH. (eds) Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85068-7_11
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