A Genetic Algorithm for Function Optimization
نویسندگان
چکیده
منابع مشابه
Towards a Genetic Algorithm for Function Optimization
This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function optimization, which is simple and reliable for most applications. The novelty in current approach is random provision of parameters, created by the GA. Chromosome portions which do not t ranslate into fitness are given function to diversify control parameters for the GA, providing random parameter sett...
متن کاملA Genetic Algorithm for Function Optimization: A Matlab Implementation
A genetic algorithm implemented in Matlab is presented. Matlab is used for the following reasons: it provides many built in auxiliary functions useful for function optimization; it is completely portable; and it is eecient for numerical computations. The genetic algorithm toolbox developed is tested on a series of non-linear, multi-modal, non-convex test problems and compared with results using...
متن کاملErratum: A Species Conserving Genetic Algorithm for Multimodal Function Optimization
This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into t...
متن کاملA Grid-based Genetic Algorithm for Multimodal Real Function Optimization
A novel genetic algorithm called GGA (Grid-based Genetic Algorithm) is presented to improve the optimization of multimodal real functions. The search space is discretized using a grid, making the search process more efficient and faster. An integer-real vector codes the genotype and a GA is used for evolving the population. The integer part allows us to explore the search space and the real par...
متن کاملA Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization
This paper presents a cumulative multi-niching genetic algorithm (CMN GA), designed to expedite optimization problems that have computationally-expensive multimodal objective functions. By never discarding individuals from the population, the CMN GA makes use of the information from every objective function evaluation as it explores the design space. A fitness-related population density control...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEJ Transactions on Electronics, Information and Systems
سال: 2002
ISSN: 0385-4221,1348-8155
DOI: 10.1541/ieejeiss1987.122.3_363