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Evolutionary algorithms are heuristic methods that operate on a population of individuals, which represent candidate solutions to optimization problems. Individuals with better quality, or fitness, are reproduced at a higher rate than less fit individuals ...
In this paper we describe a new methodology for optimising building and urban geometric forms for the utilisation of solar irradiation, whether by passive or active means. For this we use a new evolutionary algorithm(a hybrid CMA-ES/HDE algorithm) to searc ...
The determination of transcriptional regulatory networks is key to the understanding of biological systems. However, the experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-consuming, while current ...
The purpose of this paper is to analyse the optimal three-dimensional form of buildings that minimise energy consumption due to solar irradiation. We use an evolutionary algorithm (hybrid CMA-ES/HDE algorithm) already applied to maximise solar energy utili ...
Evolutionary algorithms were proposed to automatically find solutions to computational problems, much like evolution discovers new adaptive traits. Lately, they have been used to address challenging questions about the evolution of modularity, the genetic ...
The paper describes and discusses the application of a guided evolutionary approach for the optimization of water distribution networks. Initially developed for the Battle of the Water Networks II (BWN-II), the approach was adapted and improved for competi ...
1 Introduction to energy systems and evolutionary algorithms. 2 Genetic Algorithm for Constrained Optimization Models. 3 A Framework for Multi-Objective Evolutionary Algorithms. 4 Thermo-Economic Optimisation of a Solar Thermal Plant. ...
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but ...
Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have been shown to be a prom ...
Institute of Electrical and Electronics Engineers2013
The manual design of adaptive controllers for robotic systems that face unpredictable environmental changes is often challenging. There is thus a growing interest in the development of automatic design tools to assist control engineers. One of the most com ...