Evolutionary algorithms and their applications to engineering problems
A Slowik, H Kwasnicka - Neural Computing and Applications, 2020 - Springer
The main focus of this paper is on the family of evolutionary algorithms and their real-life
applications. We present the following algorithms: genetic algorithms, genetic programming …
applications. We present the following algorithms: genetic algorithms, genetic programming …
A review of evolutionary multimodal multiobjective optimization
R Tanabe, H Ishibuchi - IEEE Transactions on Evolutionary …, 2019 - ieeexplore.ieee.org
Multimodal multiobjective optimization aims to find all Pareto optimal solutions, including
overlap** solutions in the objective space. Multimodal multiobjective optimization has …
overlap** solutions in the objective space. Multimodal multiobjective optimization has …
Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review
M Janga Reddy, D Nagesh Kumar - h2oj, 2020 - iwaponline.com
During the last three decades, the water resources engineering field has received a
tremendous increase in the development and use of meta-heuristic algorithms like …
tremendous increase in the development and use of meta-heuristic algorithms like …
A niching memetic algorithm for multi-solution traveling salesman problem
Multi-solution problems extensively exist in practice. Particularly, the traveling salesman
problem (TSP) may possess multiple shortest tours, from which travelers can choose one …
problem (TSP) may possess multiple shortest tours, from which travelers can choose one …
An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions
Over the recent years, continuous optimization has significantly evolved to become the
mature research field it is nowadays. Through this process, evolutionary algorithms had an …
mature research field it is nowadays. Through this process, evolutionary algorithms had an …
Hybridizing niching, particle swarm optimization, and evolution strategy for multimodal optimization
Multimodal optimization problems (MMOPs) are common problems with multiple optimal
solutions. In this article, a novel method of population division, called nearest-better …
solutions. In this article, a novel method of population division, called nearest-better …
Discovering the elite hypervolume by leveraging interspecies correlation
V Vassiliades, JB Mouret - Proceedings of the Genetic and Evolutionary …, 2018 - dl.acm.org
Evolution has produced an astonishing diversity of species, each filling a different niche.
Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of …
Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of …
A niching indicator-based multi-modal many-objective optimizer
R Tanabe, H Ishibuchi - Swarm and Evolutionary Computation, 2019 - Elsevier
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal
solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective …
solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective …
BIM reconstruction from 3D point clouds: A semantic registration approach based on multimodal optimization and architectural design knowledge
Reconstructing semantically rich building information model (BIM) from 2D images or 3D
point clouds represents a research realm that is gaining increasing popularity in …
point clouds represents a research realm that is gaining increasing popularity in …
Static and dynamic multimodal optimization by improved covariance matrix self-adaptation evolution strategy with repelling subpopulations
The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-
CMSA-ES) is one of the most successful multimodal optimization (MMO) methods currently …
CMSA-ES) is one of the most successful multimodal optimization (MMO) methods currently …