genetic algorithm

Introduction:

Professor John Holland in 1975 proposed an attractive class of computational models, called Genetic Algorithms (GA), that simulate the biological evolution process for solving problems in a wide domain. GA has three main applications, namely, intelligent search, optimization and machine and robot learning. Currently, GA is used along with neural nets and fuzzy logic for solving more complex problems. Because of their joint usage in many problems, these together are often referred to by a generic name: “soft-computing”.
2.1Genetic Algorithm:
Genetic algorithms are adaptive heuristic search algorithm premised on the Darwin’s evolutionary ideas of natural selection and genetic. The basic concept of genetic algorithms is designed to simulate processes in natural system necessary for evolution. Strength of Genetic Algorithm comes from:
1. Genetic algorithms are intrinsically parallel. They can explore the solution space in multiple directions at once. So convergence to an optimal solution does not depend on the chosen initial solution.
2. They are well-suited to solving problems having huge search space.
3. They perform well in problems for which the fitness landscape is complex - where the function is discontinuous, noisy, changes over time, or has many local optima.
Genetic algorithm maintains a population of individuals, for generation. Each individual represents a potential solution to the problem. Each individual is evaluated to give some measure of its fitness. Some individuals undergo random transformations by means of genetic operations to form new individuals. There are two type of transformation:
Mutation, which forms new individuals by making changes in a single individual, and Crossover, which forms new individuals by combining parts from two individuals.