Evolutionary Algorithms
In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. Artificial evolution (AE) describes a process involving individual evolutionary algorithms; EAs are individual components that participate in an Artificial Evolution [ref].
The most extensive Evolutionary Algorithm (EA) has started billions of years ago with the Big Bang. Thanks to it humanity evolved just recently and is able to enjoy all those other mind-blowing things that arose at the same time. Thus it is no wonder that one day someone had the idea to model the principles of evolution and use it for solving complex problems. Evolutionary Algorithm (EA) is the umbrella term for all computational models that are inspired by evolutionary mechanisms. There is a variety of implementations of EAs available. The most important ones are Genetic Algorithms (GAs), Genetic Programming (GP), Evolutionary Programming (EP), Classifier Systems and Artificial Life (AI). Although the particular representations can heavily differ from each other they all share basic principles. Every algorithm organizes a population of individuals (e.g. construction plans, configuration data, DNA ...). The individuals are developed through mutation and crossover operations and then evaluated by measuring their fitness that indicates how good they fit into their environment. Problems to be solved are defined by the shape of the fitness landscape [ref].
In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. Artificial evolution (AE) describes a process involving individual evolutionary algorithms; EAs are individual components that participate in an Artificial Evolution [ref].
The most extensive Evolutionary Algorithm (EA) has started billions of years ago with the Big Bang. Thanks to it humanity evolved just recently and is able to enjoy all those other mind-blowing things that arose at the same time. Thus it is no wonder that one day someone had the idea to model the principles of evolution and use it for solving complex problems. Evolutionary Algorithm (EA) is the umbrella term for all computational models that are inspired by evolutionary mechanisms. There is a variety of implementations of EAs available. The most important ones are Genetic Algorithms (GAs), Genetic Programming (GP), Evolutionary Programming (EP), Classifier Systems and Artificial Life (AI). Although the particular representations can heavily differ from each other they all share basic principles. Every algorithm organizes a population of individuals (e.g. construction plans, configuration data, DNA ...). The individuals are developed through mutation and crossover operations and then evaluated by measuring their fitness that indicates how good they fit into their environment. Problems to be solved are defined by the shape of the fitness landscape [ref].
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| Evolutionary Algorithms |
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