Human intelligence is unparallel and it is using this intelligence we are working towards the concept of artificial intelligence, we are achieving success, still a lot of research and activities are going on in this field. There are various schemes that scientists and enthusiasts all over the world are working on, these are Neural Networks, Fuzzy logic and genetic algorithms.
Neural Networks involve a learning mechanism which is explained in manner the human brain works. Fuzzy logic can be seen as the principle basis used by various companies to develop day to day machines like washing machine, air conditioners. Genetic algorithms if we think of is more of an optimization.
Genetic algorithms if we think of is more of an optimization. It uses the best parameters in a set of events, outcomes and results and extracts the best result out of it. It is based on concept of Human evolution i.e, the way human species developed and evolved with passing time.[GeneticAlgorithms.ppt] .
A Genetic Algorithm (GA) is a robust optimization technique based on natural selection. The basic goal of GAs is to optimize functions called fitness functions. GA-based approaches differ from conventional problem-solving methods in several ways. First, GAs work with a coding of the parameter set rather than the parameters themselves. Second, GAs search from a population of points rather than a single point. Third, GAs use payoff (objective function) information, not other auxiliary knowledge. Finally, GAs use probabilistic transition rules, not deterministic rules. These properties make GAs robust, powerful, and data-independent.
Its basis in natural selection allows a GA to employ a “survival of the fittest” strategy when searching for optima. The use of a population of points helps the GA avoid converging to false peaks (local optima) in the search space.
Chromosome: A simple GA requires the parameter set of the optimization problem to be encoded as a string (binary, real, etc.). These strings are known as chromosomes. They are manipulated by the GA in an attempt to obtain the string that represents the optimal solution to the problem. Evolutionary process works on chromosomes, i.e., chromosomes are used to pass information from one generation to the next and also for information exchange between two individuals of the same generation to create a new individual consisting of a combination of information from both the parent individuals.
Genes: A character or symbol in a GA chromosome is called as a gene. Genes are the basic building blocks of the solution and represent the properties which make one solution different from the other.
Allele: The value of a gene in a GA is called an allele, such as for eye color, the different possible ‘settings’ (e.g., blue, brown, hazel etc.) are called alleles.
Selection: A genetic operator used to select individuals for reproduction .
Crossover: A key operator used in the GA to create new individuals by combining portions of two parent strings .
Crossover probability: Probability of performing a crossover operation, denoted by pc, i.e., the ratio of the number of offspring produced in each generation to the population size. This value of pc is chosen generally in the range of 0.7 to 0.9.
Mutation: An incremental change to a member of the GA population .
Mutation Probability: The probability of mutating each gene in a GA chromosome, denoted by pm. This value is chosen generally in the range of 0.01 to 0.03.
B GA Basics
A simple GA starts with a population of solutions encoded in one of the many ways. Binary encodings are quite common and are used in this thesis. The GA determines each string’s strength based on an objective function and performs one or more of the three genetic operators on certain strings in the population , .
select a pair of members randomly
perform crossover on the two members
perform mutation on the crossed members
insert the new members into the population
until done with current generation
until done with GA
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