Genetic algorithm v/s other Search & Optimization Methods


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Genetic Algorithm (GA)

GAs work with a population of candidate solutions and not a single point.

GAs work with coding of parameters instead of parameters themselves.

GAs do not require any domain knowledge (gradient information etc.) and just use the payoff information.

GAs are stochastic methods, i.e., use probabilistic transition rules and not deterministic ones.

Applies to a variety of problems and not works in a restricted domain.

Multiple solutions can be obtained without extra effort.

GAs are implicitly parallel and can be implemented on parallel machines.

GAs are quite successful in locating the regions containing optimal solution(s), if not the optimum solution itself.

GAs can solve problems involving large time domain.

Some applications of GA

Optimization

Automatic Programming

Machine Learning

Economics

Immune systems

Ecology

Population genetics

Evolution and learning

Social systems

Bioinformatics

Neural Networks & Fuzzy Logic

Source

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3 Comments Add yours

  1. yvens auguste says:

    j’ai connait tout les information de luis..

  2. no it’s from a ppt file.. i have put the source there, you can refer to it..

  3. Bruno says:

    🙂 finn appran par coeur ca?

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