Summary
Linear ordering problem is a well-known optimization problem attractive for its complexity (it is an NP-hard problem), rich library of test data and variety of real world applications. In this paper, we investigate the use and performance of two variants of genetic algorithms, mutation only genetic algorithms and higher level chromosome genetic algorithm, on the linear ordering problem. Both methods are tested and evaluated on a library of real world and artificial linear ordering problem instances.
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Genetic Algorithms for the Linear Ordering Problem
1. Introduction
Genetic algorithms (GAs) are probably the most popular and widespread member of the class of evolutionary algorithms (EAs). EAs found a group of iterative stochastic search and optimization methods based on mimicking successful optimization strategies observed in nature [6, 11, 4]. The essence of EAs lies in their emulation of Darwinian evolution, utilizing the concepts of Mendelian inheritance for use in computer science [4]. Together with fuzzy sets, neural networks, and fractals, evolutionary algorithms are among the fundamental members of the class of soft computing methods.Genetic algorithms are a widely applied and highly successful EA variant based on computer emulation of genetic evolution. Genetic algorithms have been successfully used to solve non- trivial multimodal optimization problems. They inherit the robustness of emulated natural optimization processes and excel in browsing huge, potentially noisy problem domains. Their clear principles, ease of interpretation, intuitive and reusable practical use and significant results made genetic algorithms the method of ch...See the full content of this document
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