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   +(36) 88 624 023 | |    H-8200, Veszprem, Egyetem str. 10, Building I.

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Ágnes Vathy-Fogarassy, PhD; Tibor Dulai, PhD


Topics covered:

Students will learn about the following topics, including their individual training plans and interests:

T1. Local searches, trajectory-based algorithms, and population-based algorithms:

  • the aims, operations, and characteristics of various search methods
  • local search methods
  • characteristics of trajectory-based algorithms (e.g. tabu search, simulated annealing)
  • population-based algorithms (e.g. PSO, ant colony optimization, evolutionary algorithms)

T2. Improving the efficiency of genetic algorithms:

  • solutions and reasons for applying advanced representations, genetic operators, and strategies (such as crossover, mutation, and selection methods)
  • adaptive genetic algorithms: adaptive parameter and operator selection
  • self-adaptive GA
  • hybrid GA: how and when to integrate GA with other metaheuristics or heuristics

T3. Multi-objective and Constraint Handling GA:

  • the concept of Pareto front
  • Pareto dominance, fitness assignment methods: e.g. NSGA-II, SPEA2
  • constraint handling with GA: penalty-based approaches, repair mechanisms
  • handling complex and conflicting objectives


1.      David E. Goldberg: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, 1989.

2.      K. Deb, A. Pratap, S. Agarwal, T. Meyarivan: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II., IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197., 2002.