In model-based evolutionary algorithms (EAs), the underlying search distribution is adapted to the problem at hand, for example based on dependencies between decision variables. Hill-valley clustering is an adaptive niching method in which a set of solutions is clustered such that each cluster corresponds to a single mode in the fitness landscape. This can be used to adapt the search distribution of an EA to the number of modes, exploring each mode separately. Especially in a black-box setting, where the number of modes is a priori unknown, an adaptive approach is essential for good performance. In this work, we introduce multi-objective hill-valley clustering and combine it with MAMaLGaM, a multi-objective EA, into the multi-objective hill-valley EA (MO-HillVallEA). We empirically show that MO-HillVallEA outperforms MAMaLGaM and other well-known multi-objective optimization algorithms on a set of benchmark functions. Furthermore, and perhaps most important, we show that MO-HillVallEA is capable of obtaining and maintaining multiple approximation sets simultaneously over time.

Additional Metadata
Keywords Multi-modal optimization, Multi-objective optimization, Niching
Persistent URL dx.doi.org/10.1145/3321707.3321759
Conference Genetic and Evolutionary Computation Conference
Citation
Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2019). Real-valued evolutionary multi-modal multi-objective optimization by hill-valley clustering. In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 568–576). doi:10.1145/3321707.3321759