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Evolutionary algorithms in theory and practice
TLDR
In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.About:
The article was published on 1996-01-01 and is currently open access. It has received 2679 citations till now. The article focuses on the topics: Evolutionary music & Evolutionary programming.read more
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Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm
Chia-Feng Juang,Chao-Hsin Hsu +1 more
TL;DR: Online adaptive temperature control by field-programmable gate array (FPGA) - implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper and the incorporated Simplex method helps find a better solution around the local region of the best solution found by PSO so far.
Parallel Estimation of Distribution Algorithms
TL;DR: The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem.
Patent
Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis
Piero P. Bonissone,Srinivas Bollapragada,Kete Chalermkraivuth,Neil Holger White Eklund,Naresh Iyer,Rajesh Venkat Subbu +5 more
TL;DR: In this article, a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem is presented. But this method is not suitable for the problem of multi-dimensional portfolio optimization.
Journal ArticleDOI
Modified grasshopper optimization framework for optimal power flow solution
TL;DR: Simulation results reveal the better performance and superiority of the proposed technique to solve various OPF problems compared with well-recognized evolutionary optimization techniques stated in the literature review.
Journal ArticleDOI
Error thresholds in genetic algorithms
TL;DR: This work uses a genetic algorithm, instead of the quasispecies model, as the underlying model of evolution, and investigates whether the phenomenon of error thresholds is found on finite populations of bit strings evolving on complex landscapes, and finds that error thresholds depend mainly on the selection pressure and genotype length.