An overview of genetic algorithms: Part 1

Genetic Algorithms (GAs) are adaptive methods which may be used to solve search and optimisation problems. They are based on the genetic processes of biological organisms. Over many generations, natural populations evolve according to the principles of natural selection and \survival of the ttest", rst clearly stated by Charles Darwin in The Origin of Species. By mimicking this process, genetic algorithms are able to \evolve" solutions to real world problems, if they have been suitably encoded. For example, GAs can be used to design bridge structures, for maximum strength/weight ratio, or to determine the least wasteful layout for cutting shapes from cloth. They can also be used for online process control, such as in a chemical plant, or load balancing on a multi-processor computer system. The basic principles of GAs were rst laid down rigourously by Holland [Hol75], and are well described in many texts (e.g. [Dav87, Dav91, Gre86, Gre90, Gol89a, Mic92]). GAs simulate those processes in natural populations which are essential to evolution. Exactly which biological processes are essential for evolution, and which processes have little or no role to play is still a matter for research; but the foundations are clear. In nature, individuals in a population compete with each other for resources such as food, water and shelter. Also, members of the same species often compete to attract a mate. Those individuals which are most successful in surviving and attracting mates will have relatively larger numbers of o spring. Poorly performing individuals will produce few of even no o spring at all. This means that the genes from the highly adapted, or \ t" individuals will spread to an increasing number of individuals in each successive generation. The combination of good characteristics from di erent ancestors can sometimes produce \super t" o spring, whose tness is greater than that of either parent. In this way, species evolve to become more and more well suited to their environment. GAs use a direct analogy of natural behaviour. They work with a population of \individuals", each representing a possible solution to a given problem. Each individual is assigned a \ tness score" according to how good a solution to the problem it is. For example, the tness score might be the strength/weight ratio for a given bridge design. (In nature this is equivalent to assessing how e ective an organism is at competing for resources.) The highly t individuals are given opportunities to \reproduce", by \cross breeding" with other

[1]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[2]  Gunar E. Liepins,et al.  Greedy Genetics , 1987, ICGA.

[3]  Terence C. Fogarty,et al.  Rule-based optimization of combustion in multiple-burner furnaces and boiler plants , 1988 .

[4]  John J. Grefenstette,et al.  Improving tactical plans with genetic algorithms , 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.

[5]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[6]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[7]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[8]  David E. Goldberg,et al.  Sizing Populations for Serial and Parallel Genetic Algorithms , 1989, ICGA.

[9]  David E. Goldberg,et al.  Finite Markov Chain Analysis of Genetic Algorithms , 1987, ICGA.

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

[11]  David H. Ackley,et al.  An empirical study of bit vector function optimization , 1987 .

[12]  B. D. Bunday,et al.  Basic optimisation methods , 1985, Mathematical Gazette.

[13]  K. De Jong.  Adaptive System Design: A Genetic Approach , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  John J. Grefenstette,et al.  Genetic algorithms and their applications , 1987 .

[15]  Martina Gorges-Schleuter,et al.  ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy , 1989, ICGA.

[16]  Rob A. Rutenbar,et al.  Simulated annealing algorithms: an overview , 1989, IEEE Circuits and Devices Magazine.

[17]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[18]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[19]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[20]  L. Darrell Whitley,et al.  Using Reproductive Evaluation to Improve Genetic Search and Heuristic Discovery , 1987, ICGA.

[21]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[22]  Kenneth de Jong,et al.  Adaptive System Design: A Genetic Approach , 1980, IEEE Trans. Syst. Man Cybern..

[23]  Kazuhiko Kawamura,et al.  Exploring Problem-Specific Recombination Operators for Job Shop Scheduling , 1991, ICGA.

[24]  L. Darrell Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, International Conference on Genetic Algorithms.

[25]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[26]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[27]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[28]  Craig Caldwell,et al.  Tracking a Criminal Suspect Through "Face-Space" with a Genetic Algorithm , 1991, ICGA.

[29]  Michael P. Fourman,et al.  Compaction of Symbolic Layout Using Genetic Algorithms , 1985, ICGA.

[30]  Kenneth A. De Jong,et al.  Using Genetic Algorithms to Solve NP-Complete Problems , 1989, ICGA.

[31]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[32]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[33]  Anne Brindle,et al.  Genetic algorithms for function optimization , 1980 .

[34]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Theoretical Considerations , 1987, ICGA.

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

[36]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[37]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.