Publication

A Fast Multi-Objective Evolutionary Algorithm applied to Industrial Problems

Daniel Favrat
2002
Conference Papers

Abstract

The CEPA, an Evolutionary Algorithm that preserves diversity by finding clusters in the population, has had its convergence performance improved by a technique tentatively called ‘evolutionary operator selection’. Performance is compared to results found in the literature, though at the moment it is not entirely clear how the evolutionary operator selection mechanisms work. The resulting algorithm has been applied to a number of problems - including a hybrid vehicle configuration and coke production in Shanxi Province, China.

Official source

https://infoscience.epfl.ch/entities/publication/b9884f96-4caf-473e-82ca-68fe852117d0

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Daniel Favrat
2002
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/b9884f96-4caf-473e-82ca-68fe852117d0

About this result

Related concepts (31)

Evolutionary algorithm

In computational intelligence (CI), 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).

Evolutionary computation

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated.

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). 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. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

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