Evolutionary algorithm

Function (biology)

Process (computing)

Biology

Fitness function

Basis (linear algebra)

Operating system

Computer science

Mathematical optimization

Evolutionary biology

Mathematics

Genetic algorithm

Artificial intelligence

Geometry

Fitness approximation

Machine learning

A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluation speeds of the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.

DAFHEA: a dynamic approximate fitness-based hybrid EA for optimisation problems