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:

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International Conference

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ICSI 2016 | The Seventh International Conference on Swarm Intelligence, Bali, Indonesia

ICSI 2016 | The Seventh International Conference on Swarm Intelligence…

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SCI 2017 | International Conference on Smart Computing and Informatics

SCI 2017 | International Conference on Smart Computing and Informatics

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ICSI 2017 | The Eighth International Conference on Swarm Intelligence, Fukuoka, Japan

ICSI 2017 | The Eighth International Conference on Swarm Intelligence,…

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The 2017 IEEE Symposium Series on Computational Intelligence (SSCI)

The 2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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2018 IEEE World Congress on Computational Intelligence (WCCI 2018)

2018 IEEE World Congress on Computational Intelligence (WCCI 2018)

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