With the explosion of data generation, getting optimal solutions to data driven problems is
increasingly becoming a challenge, if not impossible. It is increasingly being recognised that …

The application of evolutionary algorithms (EAs) in multiobjective optimization is currently
receiving growing interest from researchers with various backgrounds. Most research in this …

This paper introduces a novel bio-inspired meta-heuristic optimization algorithm, named
termite life cycle optimizer (TLCO), which is based on both the life cycle of a termite colony …

One approach to the design of learning systems is to extract heuristics from existing adaptive
systems. Genetic algorithms are heuristic learning models based on principles drawn from …

This paper is the result of a literature study carried out by the authors. It is a review of the
different attempts made to solve the Travelling Salesman Problem with Genetic Algorithms …

Metaheuristics are the most exciting development in approximate optimization techniques of
the last two decades. They have had widespread successes in attacking a variety of difficult …

This paper describes how evolutionary techniques of variation and selection can be used to
create complex simulated structures, textures, and motions for use in computer graphics and …

Genetic algorithms (GAs) have become popular as a means of solving hard combinatorial
optimization problems. The first part of this chapter briefly traces their history, explains the …

Rapid developments in the field of genetic algorithms along with the popularity of the first
edition precipitated this completely revised, thoroughly updated second edition of The …

The genetic algorithm is a general purpose, population-based search algorithm in which the
individuals in the population represent samples from the set of all possibilities, whether they …