Memetic Algorithms (MAs)

Memetic Algorithms (MAs) are basically Evolutionary Algorithms (EAs) with local search plugged in. In the most basic form, the EA is “outer” or “main” algorithm. To each solution that it samples, a local search is applied. The result of the local search then enters the population.

Publications

• Bo YUAN (袁博), Bin LI (李斌), Thomas Weise (汤卫思), and Xin YAO (姚新): A New Memetic Algorithm with Fitness Approximation for the Defect-Tolerant Logic Mapping in Crossbar-Based Nanoarchitectures. IEEE Transactions on Evolutionary Computation (IEEE-TEVC) 18(6):846-859. December 2014.
  MAs, more infos.
• Yan JIANG (江炎), Thomas Weise (汤卫思), Jörg Lässig, Raymond Chiong, and Rukshan Athauda: Comparing a Hybrid Branch and Bound Algorithm with Evolutionary Computation Methods, Local Search and their Hybrids on the TSP. IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS'2014), part of the IEEE Symposium Series on Computational Intelligence (SSCI'2014), December 9-12, 2014, Orlando, FL, USA, pages 148-155. Los Alamitos, CA, USA: IEEE Computer Society Press.
  TSP, TSP Suite, logistics, local search, B&B, EAs, MAs, more infos.
• Thomas Weise (汤卫思), Raymond Chiong, Jörg Lässig, Ke TANG (唐珂), Shigeyoshi Tsutsui, Wenxiang CHEN (陈文祥), Zbigniew Michalewicz, and Xin YAO (姚新): Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. IEEE Computational Intelligence Magazine (CIM) 9(3):40-52. August 2014.
  TSP, TSP Suite, logistics, local search, EAs, MAs, ACO, EDAs, FFA, more infos.