Fitness Approximation In Evolutionary Computation - a Survey.
Yaochu Jin, Bernhard Sendhoff
Abstract
Open-access reader
Yaochu Jin, Bernhard Sendhoff
Abstract
Open-access reader
Fitness evaluation is a basic operation in evolutionary computation. However, an explicit fitness function is not always available in real-world applications. In many cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a short survey on fitness approximation in evolutionary computation is given. Main issues like approximation models, model management schemes, learning methods as well as data sampling techniques are presented. Finally, interesting open problems are discussed.
OpenAlex reports 53 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Fitness evaluation is a basic operation in evolutionary computation. However, an explicit fitness function is not always available in real-world applications. In many cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a short survey on fitness approximation in evolutionary computation is given. Main issues like approximation models, model management schemes, learning methods as well as data sampling techniques are presented. Finally, interesting open problems are discussed.
Key concepts: Fitness approximation, Fitness function, Evolutionary computation, Interactive evolutionary computation, Computation, Computer science, Evolutionary algorithm, Mathematical optimization