A New Improved Genetic Algorithms and its Property Analysis

In order to deal with constraints in the optimization problems, there are a few improved genetic algorithms (namely GAs). These GAs have lots of advantages and their applications respectively. But disadvantages in common are that they are strongly dependent on the optimization problem and have narro...

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Bibliographic Details
Main Authors: Yu-gen Gao, De-yu Song
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Automotive engineering
Constrained Optimization
Constraint optimization
Convergence Property
Genetic algorithms
Genetic engineering
Genetic mutations
Mechanical factors
Optimization methods
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Summary:In order to deal with constraints in the optimization problems, there are a few improved genetic algorithms (namely GAs). These GAs have lots of advantages and their applications respectively. But disadvantages in common are that they are strongly dependent on the optimization problem and have narrow applications. Studied based on existing methods, a new improved genetic algorithms based on converting infeasible individuals into feasible ones (hereinafter shorted as the CIFGA) is proposed in this paper. The main principle of the CIFGA is that every infeasible individual must be converted compulsively into feasible one in every generation and the population size keep unchanged. The CIFGA, with either binary coding or real coding, is also proved to converge to global optimum solution. The on-line and off-line performances show that compare with other GAs, the CIFGA has a great advantage on convergence property and has good ability of solving constrained optimization in general purpose.
DOI:10.1109/WGEC.2009.150