Evolutionary Computation Intelligent Systems

Welcome to the Evolutionary Computational Intelligent Systems Lab (ECIS) directed by Prof. Rammohan Mallipeddi. ECIS is hosted by the School of Electronics Engineering Department of the Kyungpook National University, Daegu, South Korea. Evolutionary Computation is a family of algorithms that are inspired by biological evolution for the global optimization . Technically, Evolutionary Computation consists of a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In our lab, we develop evolutionary algorithms (EAs), to solve the real world problems associated single and multiple objectives. Evolutionary algorithms are the subset of evolutionary computation which generally adopt techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. In the past few years we have developed various EAs to solve the benchmark problems such as Differential Evolution for the single-objective optimization and Multi-objective Evolutionary Algorithms for optimizing the multi and many-objectives. Along with that we also concentrate on developing the EAs for the applications of: