Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

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.