In this way, over many generations, good characteristics are spread throughout the population. This new generation contains a higher proportion of the characteristics possessed by the good members of the previous generation.
A whole new population of possible solutions is thus produced by selecting the best individuals from the current "generation", and mating them to produce a new set of individuals. The least fit members of the population are less likely to get selected for reproduction, and so "die out. This produces new individuals as "offspring", which share some features taken from each "parent". The highly-fit individuals are given opportunities to "reproduce", by "cross breeding" with other individuals in the population. Each individual is assigned a "fitness score" according to how good a solution to the problem it is. GA's work with a population of "individuals", each representing a possible solution to a given problem. The basic principles of Gas were first laid down rigorously (1). By mimicking this process, genetic algorithms are able to "evolve" solutions to real world problems, if they have been suitably encoded. Over many generations, natural populations evolve according to the principles of natural selection and "survival" of the fittest. They are based on the genetic processes of biological organisms. Genetic algorithms (GA's) are adaptive methods that may be use to solve search and optimization problems. A detailed illustrative examples is presented to demonstrate that how to solve Traveling Salesman Problem (TSP) and Drawing the largest possible circle in a space of stars without enclosing any of them.
This approach is based primarily on using MATLAB in implementing the genetic operators: initialization, crossover, mutation, evaluation and selection. In this paper, an attractive approach for teaching genetic algorithm (GA) is presented.
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