This class implements base methods to apply genetic algorithms to arbitrary subjects.
Its functions can be call instantiating a class object or called static methods.
The class can perform several types of generic algorithm operations like crossover, mutation, selection and death over populations of any objects of the same class.
It can be used as a base implementation of genetic algorithms to solve many types of problems on which the best solution may be found through natural selection.
September 2005
Winner
Prize: One book of choice by SAMS |
Genetic Algorithms are computing algorithms used to find approximate solutions to optimization problems. These problems consist in determining sequences of data that may provide better results depending on the order of the data elements.
The genetic algorithms are inspired in the evolutionary biology theories that determine that on each generation the individuals tend to evolve and address better the problems that challenge their survival.
Evolution happens after performing slight mutations to the chromosome sequence of the individuals of the current generation. The best fit individuals will survive and become the origin of the next generation.
In computing genetic algorithms can be used to optimize solutions for problems like job scheduling, artificial intelligence for computer operated game characters, compact file storage, etc..
This class can be used as a framework to implement PHP applications that can benefit of the use of genetic algorithms.
Manuel Lemos |
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