Viability evolutionary algorithms and applications to neuroscience and biology
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Viability Evolution is an abstraction of artificial evolution which operates by eliminating candidate solutions that do not satisfy viability criteria. Viability criteria are defined as boundaries on the values of objectives and constraints of the problem ...
This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimization tools in net zero energy buildings (NZEB) design. The paper reviews current trends in simulation-based building performance optimization ...
The design of a microvascular flow network embedded in an actively-cooled polymeric material is presented. A multi-objective Genetic Algorithm (GA) combined with the finite element method is first used to determine the quasi-optimized network configuration ...
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 ...
Proteins have the ability to assemble in multimeric states to perform their specific biological function. Unfortunately, characterizing experimentally these structures at atomistic resolution is usually difficult. For this reason, in silico methodologies a ...
Associative memory problem: Find the closest stored vector (in Hamming distance) to a given query vector. There are different ways to implement an associative memory, including the neural networks and DNA strands. Using neural networks, connection weights ...
Capillary zone electrophoresis (CZE) and matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) are two techniques highly suitable for the separation and detection of intact proteins. Herein, based on the use of a recently introduced iont ...
A novel hybrid evolutionary neural network method to generate multiple spectrum-compatible artificial earthquake accelerograms (SCAEAs) is presented. Genetic algorithm is employed to optimize the weight values of networks. In order to improve the training ...
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 ...
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. ...
