Genetic algorithms
- 2. Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near- optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
- 3. The algorithm begins by random initial population. The algorithm then creates a sequence of new populations. At each step, the algorithm uses the individuals in the current generation to create the next population. : DIAGRAM:
- 4. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. They are Robust Provide optimization over large space state. Unlike traditional AI, they do not break on slight change in input or presence of noise
- 5. Is faster and more efficient as compared to the traditional methods. Has very good parallel capabilities. Provides a list of “good” solutions and not just a single solution. Always gets an answer to the problem, which gets better over the time.
- 6. The population of individuals are maintained within search space. Each individual represent a solution in search space for given problem. Each individual is coded as a finite length vector (analogous to chromosome) of components. These variable components are analogous to Genes. Thus a chromosome (individual) is composed of several genes (variable components).
- 8. Population − It is a subset of all the possible (encoded) solutions to the given problem. The population for a GA is analogous to the population for human beings except that instead of human beings, we have Candidate Solutions representing human beings. Chromosomes − A chromosome is one such solution to the given problem. Gene − A gene is one element position of a chromosome. Allele − It is the value a gene takes for a particular chromosome.
- 9. Genetic algorithms have many applications, some of them are – Recurrent Neural Network Mutation testing Code breaking Filtering and signal processing Learning fuzzy rule base etc