Ágnes Vathy-Fogarassy, PhD; Tibor Dulai, PhD
Prerequirements:
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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
Literature:
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.