Fitness diversity adaptation in memetic algorithms
This work proposes novel tailored implementations of Memetic Algorithm for some specific classes of problems and, at the same time, proposes novel general ideas in Computational Intelligence algorithmic philosophy. Much emphasis is given to adaptation and coordination of the local searchers. Several adaptive schemes have been designed; all of them resorting to a measurement of the fitness diversity as an estimation of the diversity amongst individuals of the population. According to the philosophy common to the algorithms included in this thesis, the algorithm should behave like an intelligent structure, thus able to analyze online the optimization process and then apply countermeasures necessary for continuing and efficiently finalizing the search. This thesis includes eight articles addressing applications having various natures such as biology, image processing, telecommunication, and electrical engineering. Each problem has been analyzed by considering the features of each fitness landscape being handled and each fitness function being optimized. The resulting algorithms seem to have promising performance in terms of final solution detected and convergence velocity. Extended numerical experiments have been carried out in each case in order to show the statistical significance of results.
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ISBN
978-951-39-8043-6Julkaisuun sisältyy osajulkaisuja
- Artikkeli I: Neri, F., Toivanen, J., & Mäkinen, R. (2007). An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV. Applied Intelligence, 27(3), 219-235. DOI: 10.1007/s10489-007-0069-8
- Artikkeli II: Neri, F., Toivanen, J., Cascella, G. L., & Ong, Y.-S. (2007). An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies. IEEE/ACM Transactions on Computational Biology and Bioinformatics Special Issue on Computational Intelligence Approaches in Computational Biology and Bioinformatics, 4(2), 264-278. DOI: 10.1109/TCBB.2007.070202
- Artikkeli III: Tirronen, V., Neri, F., Kärkkäinen, T., Valjus, K., & Rossi, T. (2007). A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production. In M. G. E. al (Ed.), Applications of Evolutionary Computing. Proceedings of EvoWorkshops 2007 (pp. 320-329). Springer. Lecture Notes in Computer Science, 4448. DOI: 10.1007/978-3-540-71805-5_35
- Artikkeli IV: Neri, F., Tirronen, V., Kärkkäinen, T., & Rossi, T. (2007). Fitness Diversity Based Adaptation in Multimeme Algorithms: A Comparative Study. In Proceedings of the IEEE Congress on Evolutionary Computation,Special Session on Memetic Algorithms, Singapore (pp. 2374-2381). DOI: 10.1109/cec.2007.4424768
- Artikkeli V: Tirronen, V., & Neri, F. (2007). A Fast Randomized Memetic Algorithm for Highly Multimodal Problems. In Proceedings of EuroGEN 2007, Jyväskylä, Finland (pp. pg. 27).
- Artikkeli VI: Neri, F., Kotilainen, N., & Vapa, M. (2007). An Adaptive Global-Local Memetic Algorithm to Discover Resources in P2P Networks. In Applications of Evolutionary Computing (pp. 61-70). Springer. Lecture Notes in Computer Science, 4448. DOI: 10.1007/978-3-540-71805-5_7
- Artikkeli VII: Neri, F., Cascella, G. L., Salvatore, N., & Stasi, S. (2007). An Adaptive Prudent-Daring Evolutionary Algorithm for Noise Handling in On-line PMSM Drive Design. In Proceedings of the IEEE Congress on Evolutionary, Special Session Evolutionary Computation in Dynamic and Uncertain Environments (pp. 584-591). IEEE. DOI: 10.1109/cec.2007.4424523
- Artikkeli VIII: Neri F., Mäkinen R.A.E. (2007). Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation. In Yang S., Ong YS., Jin Y. (eds) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol 51. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-540-49774-5_15
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