What is an evolutionary algorithm?

by Stephen M. Walker II, Co-Founder / CEO

What is an evolutionary algorithm?

An evolutionary algorithm (EA) is a type of artificial intelligence-based computational method that solves problems by mimicking biological evolution processes such as reproduction, mutation, recombination, and selection. EAs are a subset of evolutionary computation and are considered a generic population-based metaheuristic optimization algorithm.

The typical workflow of an EA involves the following steps:

  1. Initialization — An initial population of potential solutions, often referred to as individuals or members, is created. This population can be generated randomly or based on some prior knowledge of the problem.

  2. Selection — The fitness of each individual in the population is evaluated, usually through a fitness function that measures how well and how quickly it solves the problem. The fittest individuals are selected for reproduction, while the least fit ones are eliminated, mimicking the process of natural selection.

  3. Genetic Operators — The selected individuals undergo operations such as crossover (recombination) and mutation to create new candidate solutions. Crossover involves exchanging parts of two individuals to create offspring, while mutation introduces small random changes.

  4. Termination — The algorithm repeats the selection and genetic operator steps for a certain number of generations or until a satisfactory solution is found.

EAs are particularly effective at optimizing solutions and can be applied to a wide range of problems, including complex optimization problems and NP-hard problems. They are capable of exploring a vast "population" of all possible solutions, which means they are not restricted to a particular solution and can present multiple potential solutions to a problem. However, while EAs optimize effectively, they don't necessarily find the optimal solution. Instead, they constantly find working solutions and measure performance against one another.

Examples of EAs include genetic algorithms, genetic programming, evolutionary programming, and others. They have been used in various applications, from variable optimization to new designs in tools like antennas, and even in game-playing AI. Despite their effectiveness, it's important to note that EAs can have relatively high computational requirements due to the complexity of fitness determination.

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