Explaining Adaptation in Genetic Algorithms With Uniform Crossover
نویسنده
چکیده
Hyperclimbing is an intuitive, general-purpose, global optimization heuristic applicable to product spaces with rugged or stochastic cost functions. The strength of this heuristic lies in its insusceptibility to local optima when the cost function is deterministic, and its tolerance for noise when the cost function is stochastic. Hyperclimbing works by decimating a search space, i.e., by iteratively fixing the values of small numbers of variables. The hyperclimbing hypothesis posits that genetic algorithms with uniform crossover (UGAs) work by implementing efficient hyperclimbing. Proof of concept for this hypothesis comes from the use of an analytic technique that exploits algorithmic symmetry. Additionally, we present experimental results showing that a simple tweak inspired by the hyperclimbing hypothesis dramatically improves the performance of a UGA on large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin Glasses problem.
منابع مشابه
Optimization by Decimation: Explaining Adaptation in Genetic Algorithms With Uniform Crossover
We submit the hyperclimbing hypothesis—an explanation for adaptation in genetic algorithms with uniform crossover (UGAs). Hyperclimbing is a stochastic search heuristic that works by decimating a search space, i.e. by iteratively fixing the values of small numbers of search space attributes. Global decimation is known to be an effective way to approach large instances of hard constraint satisfa...
متن کاملAdaptation of Parametric Uniform Crossover in Genetic Algorithm
Exploration of the search space occurs at the cost of destructing existing good solutions. This cost will grow as the search progresses. The parametric uniform crossover is a general form of the uniform crossover operator. Using this operator, it would be possible to control the swapping probability of each locus. An adaptive method proposed that control the value of the exchange probability of...
متن کاملOn the Virtues of Parameterised Uniform Crossover
Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Theoretical results suggest that, from the view of hyperpl...
متن کاملOn the Virtues of Parameterized Uniform Crossover
Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Theoretical results suggest that, from the view of hyperpl...
متن کاملCase Studies in Applying Fitness Distributions in Evolutionary Algorithms. II. Comparing the Improvements from Crossover and Gaussian Mutation on Simple Neural Networks
Previous efforts in applying fitness distributions of Gaussian mutation for optimizing simple neural networks in the XOR problem are extended by conducting a similar analysis for three types of crossover operators. Onepoint, two-point, and uniform crossover are applied to the best-evolved neural networks at each generation in an evolutionary trial. The maximum expected improvement under Gaussia...
متن کامل