## Genetic Algorithms + Data StructuresThis book discusses a class of algorithms which rely on analogies to natural processes - algorithms based on the principle of evolution, i.e., survival of the fittest. In these algorithms, called evolution programs, a population of individuals undergo a sequence of transformations. The individuals strive for survival: a selection scheme biased towards fitter individuals selects the next generation. After some generations, the program converges and the best individual hopefully represents the optimum solution. Hence evolution programming techniques are applicable to various hard optimization problems. The book discusses constrained optimization problems in the areas of optimal control, operations research, and engineering. The problems include optimization of functions with linear constraints, the traveling salesman problem, scheduling and partitioning problems, etc. All methods are illustrated by results obtained from various experimental systems. The book collects, in a unified and comprehensive manner, the results of evolution programming techniques previously available only in widely scattered research papers. The importance of these techniques has been growing in the last decade, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. It is aimed at researchers, practitioners, and graduate students in the areas of computer science (especially artificial intelligence), operations research, and engineering. |

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applied approach arcs arithmetical crossover assume average binary representation bits Chapter chromosome chromosome representation classical genetic classifier system column component computational convergence cost crossover and mutation crossover operator crossover points data structures decoders defined discussed domain edges elements evaluation function evolution process evolution program evolution strategies example experiments floating point GAFOC GAMS gene genetic algorithm genetic operators GENETIC-2 GENOCOP system graph Graph-2 heuristic hillclimbing implementation individuals integer iteration length Machine Learning matrix methods modGA modified mutation operator nodes non-uniform mutation nonlinear NP-hard objective function optimal control optimization problems optimum parameters parents path representation performance popsize position potential solutions probability problem-specific knowledge procedure produce random number randomly repair algorithm represents schema schemata search space Section selected selector sequence simulated annealing single solution vector solve subtours techniques tion tour transportation problem traveling salesman problem variable