Abstract
Abstract. Evolutionary Algorithms (EAs) are a
fascinating branch of computational intelligence with
much potential for use in many application areas. The
fundamental principle of EAs is to use ideas inspired
by the biological mechanisms observed in nature, such
as selection and genetic changes, to find the best
solution for a given optimization problem. Generally,
EAs use iterative processes, by growing a population of
solutions selected in a guided random search and using
parallel processing, in order to achieve a desired
result. Such population based approaches, for example
particle swarm and ant colony optimization (inspired
from biology), are among the most popular metaheuristic
methods being used in machine learning, along with
others such as the simulated annealing (inspired from
thermodynamics). In this paper, we provide a short
survey on the state-of-the-art of EAs, beginning with
some background on the theory of evolution and
contrasting the original ideas of Darwin and Lamarck;
we then continue with a discussion on the analogy
between biological and computational sciences, and
briefly describe some fundamentals of EAs, including
the Genetic Algorithms, Genetic Programming, Evolution
Strategies, Swarm Intelligence Algorithms (i.e.,
Particle Swarm Optimization, Ant Colony Optimization,
Bacteria Foraging Algorithms, Bees Algorithm, Invasive
Weed Optimization), Memetic Search, Differential
Evolution Search, Artificial Immune Systems,
Gravitational Search Algorithm, Intelligent Water Drops
Algorithm. We conclude with a short description of the
usefulness of EAs for Knowledge Discovery and Data
Mining tasks and present some open problems and
challenges to further stimulate research.
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