Bell-curve based evolutionary optimization algorithm
| Autor: | Keith E. Laba, Jaroslaw Sobieszczanski-Sobieski, Rex K. Kincaid |
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| Rok vydání: | 1999 |
| Předmět: |
Mathematical optimization
Meta-optimization Control and Optimization Basis (linear algebra) Computer science Cultural algorithm Population-based incremental learning General Engineering Evolutionary algorithm Interactive evolutionary computation Computer Graphics and Computer-Aided Design Evolutionary computation Computer Science Applications Normal distribution Controllability Control and Systems Engineering Line (geometry) Genetic algorithm Probability distribution Point (geometry) Algorithm Evolutionary programming Software Mathematics |
| Zdroj: | Structural Optimization. 18:264-276 |
| ISSN: | 1615-1488 0934-4373 |
| DOI: | 10.1007/bf01223310 |
| Popis: | The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables. |
| Databáze: | OpenAIRE |
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