Реферат: One of the key questions in search for the global optimum in combinatorial optimization, non-convex mathematical programming and black-box optimization is how to overcome local optima and proceed towards a global optimum. Numerous approaches are developed to deal with this issue, and one of them is evolutionary algorithms (EAs), based on the principles of population search using selection, mutation and, sometimes, crossover operators. Recent results obtained in the theory of evolutionary algorithms provide rigorous analysis of efficient and inefficient treatment of local optima by the EAs on certain illustrative families of problem instances in the search space of binary strings. These findings are supported by experimental results on some well-known benchmarks from combinatorial optimization. In this talk, the above mentioned results will be surveyed and complemented by the new results of the author and his colleagues, obtained for one simulation-optimization problem of buffers allocation in production lines.

Библиографическая ссылка: Eremeev A.
Overcoming Local Optima in Evolutionary Heuristics: Theory and Practice
The Thirteenth International Workshop on Mathematical Models and their Applications 21-22 Nov 2024