Genetic Algorithm
Introduction
A genetic algorithm (GA) is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce the offspring of the next generation.
Overview
The process of natural selection starts with the selection of fittest individuals from a population. They produce offspring which inherit the characteristics of the parents and will be added to the next generation. If parents have better fitness, their offspring will be better than parents and have a better chance at surviving. This process continues until a generation with the maximum fitness level is found.
While the genetic algorithm is a powerful optimization technique, it does not guarantee the absolute optimal solution. It can also get stuck in local optima and may not converge to the global optimal solution. However, it is generally good at finding "reasonably good" solutions to problems.
Working Principle
Genetic algorithms are based on the ideas of natural selection and genetics. These are used to solve optimization problems and are most useful when dealing with large, complex spaces. Optimization problems involve finding the best solution from all feasible solutions, and genetic algorithms represent each solution as a genome (or chromosome), which is analogous to a point in the search space.
Genetic Representation
Each chromosome is represented using a binary string. Each bit in the string can represent some characteristic (or "gene"). For example, if we're trying to optimize a travel route between cities, each bit can represent a city.
Initialization
The first step in a genetic algorithm is to generate the initial population. This population is usually randomly generated and can be any desired size, from only a few individuals to thousands.
Fitness Function
Each individual in the population is then evaluated. This process is done using a fitness function, which evaluates how close a given solution is to the optimum. This function can vary greatly depending on the specific application of the genetic algorithm.
Selection
The next step is to select the fittest individuals to reproduce and create the next generation. There are many methods to select the best individuals, or "parents", such as roulette wheel selection, Boltzmann selection, tournament selection, etc.
Crossover
Crossover, also known as recombination, is the process of taking more than one parent solutions and producing a child solution from them. The hope is that the new solution will be better than either of the parent due to the combination of their best attributes.
Mutation
After selection and crossover, the algorithm then randomly mutates the offspring population. This involves flipping some of the bits in the binary string. Mutation is used to maintain and introduce diversity in the genetic population and is usually applied with a low probability.
Termination
The algorithm terminates if the population has converged (i.e., not much change in the population), or a maximum number of generations has been reached.
Applications
Genetic algorithms have been used in a wide variety of applications. They have been used in art and music, to create complex realistic-looking images and to create music that sounds like it was composed by a human. They are also used in biology, to model the evolution of organisms. In computer science, they are used in search algorithms and machine learning. They have also been used in economics, to model the behavior of markets.