A comprehensive survey of fitness approximation in evolutionary computation

A Comprehensive Survey of Fitness Approximation in Evolutionary Computation 33

A framework for evolutionary optimization with approximate fitness functions

It is not unusual that an approximate model is needed for fitness evaluation in evolutionary computation. In this case, the convergence properties of the evolutionary algorithm are unclear due to the approximation error of the model. In this paper, extensive empirical studies are carried out to investigate the convergence properties of an evolution strategy using an approximate fitness function...

Noisy evolutionary optimization algorithms - A comprehensive survey

Noisy optimization is currently receiving increasing popularity for its widespread applications in engineering optimization problems, where the objective functions are often found to be contaminated with noisy sensory measurements. In absence of knowledge of the noise-statistics, discriminating better trial solutions from the rest becomes difficult in the “selection” step of an evolutionary opt...

The computational complexity of N-K fitness functions

N-K fitness landscapes have been widely used as examples and test functions in the field of evolutionary computation. Thus, the computational complexity of these landscapes as optimization problems is of interest. We investigate the computational complexity of the problem of optimizing the N-K fitness functions and related fitness functions. We give an algorithm to optimize adjacent-model N-K f...

Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles

In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models can be constructed for fitness evaluations to assist the evolutionary algorithms. When approximate models are involved in evolution, it is very important to determine which individuals should be re-evaluated using the original fi...