Search Results for author: Anna V. Kononova

Found 8 papers, 0 papers with code

Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches

no code implementations29 Jun 2021 Annie Wong, Thomas Bäck, Anna V. Kononova, Aske Plaat

The combination of deep neural networks with reinforcement learning has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments.

Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution

no code implementations14 May 2021 Rick Boks, Anna V. Kononova, Hao Wang

Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly.

Emergence of Structural Bias in Differential Evolution

no code implementations10 May 2021 Bas van Stein, Fabio Caraffini, Anna V. Kononova

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved.

Is there Anisotropy in Structural Bias?

no code implementations10 May 2021 Diederick Vermetten, Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck

We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.

Differential evolution outside the box

no code implementations22 Apr 2020 Anna V. Kononova, Fabio Caraffini, Thomas Bäck

A wide range of popular Differential Evolution configurations is considered in this study.

Infeasibility and structural bias in Differential Evolution

no code implementations18 Jan 2019 Fabio Caraffini, Anna V. Kononova, David Corne

This paper thoroughly investigates a range of popular DE configurations to identify components responsible for the emergence of structural bias - recently identified tendency of the algorithm to prefer some regions of the search space for reasons directly unrelated to the objective function values.

Structural bias in population-based algorithms

no code implementations22 Aug 2014 Anna V. Kononova, David W. Corne, Philippe De Wilde, Vsevolod Shneer, Fabio Caraffini

Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty.

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