Penalty Function Methods for Constrained Optimization with Genetic Algorithms
TLDR
These penalty-based methods for handling constraints in Genetic Algorithms are presented and discussed and their strengths and weaknesses are discussed.Abstract:
Genetic Algorithms are most directly suited to unconstrained optimization. Application of Genetic Algorithms to constrained optimization problems is often a challenging effort. Several methods have been proposed for handling constraints. The most common method in Genetic Algorithms to handle constraints is to use penalty functions. In this paper, we present these penalty-based methods and discuss their strengths and weaknesses.read more
Citations
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Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics
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A parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages
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Handbook of Optimization
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A constraint-handling technique for genetic algorithms using a violation factor
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A multi-objective harmony search algorithm to optimize multi-server location–allocation problem in congested systems
TL;DR: A meta-heuristic algorithm called multi-objective harmony search algorithm (MOHA) is developed to solve the LA model, in which the facilities are modeled as an M / M / m queuing system, and the results based on different problem sizes are in favor of MOHA.
References
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TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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An efficient constraint handling method for genetic algorithms
TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
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TL;DR: In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.