%0 Generic
%A Martinez, Aritz D.
%A Fister, Iztok
%A Osaba, Eneko
%A Fister, Iztok
%A Oregi, Izaskun
%A Ser, Javier Del
%T Hybridizing differential evolution and novelty search for multimodal optimization problems
%J GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
%D 2019
%U https://hdl.handle.net/11556/2283
%X Multimodal optimization has shown to be a complex paradigm underneath real-world problems arising in many practical applications, with particular prevalence in physics-related domains. Among them, a plethora of cases within the computational design of aerospace structures can be modeled as a multimodal optimization problem, such as aerodynamic optimization or airfoils and wings. This work aims at presenting a new research direction towards efficiently tackling this kind of optimization problems, which pursues the discovery of the multiple (at least locally optimal) solutions of a given optimization problem. Specifically, we propose to exploit the concept behind the so-called Novelty Search mechanism and embed it into the self-adaptive Differential Evolution algorithm so as to gain an increased level of controlled diversity during the search process. We assess the performance of the proposed solver over the well-known CEC'2013 suite of multimodal test functions. The obtained outcomes of the designed experimentation supports our claim that Novelty Search is a promising approach for heuristically addressed multimodal problems.
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