Elitism in Java

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Elitism
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Elitism involves the selection of a set of individuals from the current generation to survive to the next generation. The number of individuals to survive to the next generation, without being mutated, is referred to as the generation gap. If the generation gap is zero, the new generation will consist entirely of new individuals. For positive generation gaps, say k, k individuals survive to the next generation. These can be the k best individuals, which will ensure that the maximum fitness value does not decrease, or k individuals, selected using any of the previously discussed selection operators.
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CHAPTER 8. INTRODUCTION TO EVOLUTIONARY COMPUTING
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Reproduction Operators
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The purpose of reproduction operators is to produce new offspring from selected individuals, either through cross-over or mutation. Cross-over is the process of creating a new individual through the combination of the genetic material of two parents. Mutation is the process of randomly changing the values of genes in a chromosome. The aim of mutation is to introduce new genetic material into an existing individual, thereby enlarging the search-space. Mutation usually occurs at a low probability. A large mutation probability distorts the genetic structure of a chromosome - the disadvantage being a loss of good genetic material in the case of highly fit individuals. Reproduction operators are usually applied to produce the individuals for the next generation. Reproduction can, however, be applied with replacement. That is, newly generated individuals replace parents if the fitness of the offspring is better than the parents; if not, the offspring do not survive to the next generation. Since cross-over and mutation operators are representation dependent, the different implementations of these operators are covered in the chapters that follow.
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General Evolutionary Algorithm
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The following pseudocode represents a general evolutionary algorithm. While this algorithm includes all operator types, different EC paradigms use different operators (as discussed in the following chapters). 1. Let g = 0 be the generation counter. 2. Initialize a population Cg of N individuals, i.e. Cg = {C g,n \n = 1, . N}. 3. While no convergence (a) Evaluate the fitness FEA(C g ,n) of each individual in population Cg (b) perform cross-over: i. select two individuals Cg , n1 and Cg,n2 ii. produce offspring from Cg,n1 and Cg,n2 (c) perform mutation i. select one individual C g,n ii. mutate Cg,n (d) select the new generation Cg+1 (e) evolve the next generation: let g = g + 1
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8.7. EVOLUTIONARY COMPUTING VS CLASSICAL OPTIMIZATION
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Convergence is reached when, for example, the maximum number of generations is exceeded an acceptable best fit individual has evolved the average and/or maximum fitness value do not change significantly over the past g generations.
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Evolutionary Computing vs Classical Optimization
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The no-free-lunch (NFL) theorem [Wolpert and Macready 1996] states that there cannot exist any algorithm for solving all problems that is on average superior to any other algorithm. This theorem motivates research in new optimization algorithms, especially EC. While classical optimization algorithms have been shown to be very successful (and more efficient than EAs) in linear, quadratic, strongly convex, unimodal and other specialized problems, EAs have been shown to be more efficient for discontinuous, nondifferentiable, multimodal and noisy problems. EC and classical optimization (CO) differ mainly in the search process and information about the search space used to guide the search process: The search process: CO uses deterministic rules to move from one point in the search space to the next point. EC, on the other hand, uses probabilistic transition rules. Also, EC uses a parallel search through search space, while CO uses a sequential search. The EC search starts from a diverse set of initial points, which allows parallel search of a large area of the search space. CO starts from one point, successively adjusting this point to move toward the optimum. Search surface information: CO uses derivative information, usually firstorder or second-order, of the search space to guide the path to the optimum. EC, on the other hand, uses no derivative information. Only the fitness values of individuals are used to guide the search.
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