Evolution Strategy: Nature’s Way of Optimization

In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. e ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. ese two phases are tied with a bridging mechanism, which plays an important role. is paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. is experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). e performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.

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“…where s(r) is given by (3) and N is the number of grasshoppers. A revised form of this formula can be used to solve optimization problem:…”

Section: Grasshopper Optimisation Algorithm: An Overviewmentioning

confidence: 99%

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

Saxena

Shekhawat

Kumar

2018

Modelling and Simulation in Engineering

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“…ES has been a successful evolutionary technique for solving complex optimization problems since the 1960s [5]. ES evolves vectors of real numbers and the "genetic" information is interchanged between these vectors through recombination operators.…”

Section: · 21 (1+l) Evolution Strategymentioning

confidence: 99%

“…The evolutionary optimization methods of the genetic algorithm (GA) [3], genetic programming (GP) [4], and evolution strategy (ES) [5] are a branch of nontraditional optimization methods drawing inspiration from the processes of natural evolution. The particle swarm optimizer (PSO), on the other hand, is inspired by the social behavior of bird flocking [6].…”

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confidence: 99%

Exploratory Toolkit for Evolutionary and Swarm-Based Optimization

Khemka¹,

Jacob²

2010

TMJ

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Optimization of parameters or "systems" in general plays an ever-increasing role in mathematics, economics, engineering, and the life sciences. As a result, a wide variety of both traditional mathematical and nontraditional algorithmic approaches have been introduced to solve challenging and practically relevant optimization problems. Evolutionary optimization methods~in the form of genetic algorithms, genetic programming, and evolution strategies~represent nontraditional optimization algorithms that draw inspiration from the processes of natural evolution. Particle swarm optimization is another set of more recently developed algorithmic optimizers inspired by social behaviors of organisms such as birds [1] and social insects. These new evolutionary approaches in optimization are now entering the stage and are becoming very successful tools for solving real-world optimization problems [2]. We present Visplore and Evolvica as a toolkit to investigate, explore, and visualize evolutionary and swarm-based optimization techniques. A webMathematica interface is also available. ‡ 1. IntroductionThe evolutionary optimization methods of the genetic algorithm (GA) [3], genetic programming (GP) [4], and evolution strategy (ES) [5] are a branch of nontraditional optimization methods drawing inspiration from the processes of natural evolution. The particle swarm optimizer (PSO), on the other hand, is inspired by the social behavior of bird flocking [6]. Recently, we have been investigating the performance of evolution-and swarm-based optimizers in the domain of biomechanics, which we developed with the Human Performance Laboratory at the Faculty of Kinesiology, University of Calgary [7|9]. In this particular biomechanical application, numerical optimization algorithms are used to design equipment for sports activities. The involved simulations of muscle movements are very time consuming and high dimensional, thus making their evaluation costly and difficult. Simulating a soccer kick is an example of a model that investigates muscle activation patterns within the leg and foot when kicking a soccer ball toward the goal. The specific objective in this case is to obtain a high ball speed, in order to minimize the goal keeper's chances of catching the ball. In 1998, Cole applied a (1+ l) ES to this model [7,8]. More recently, we presented improved adaptations of the model parameters through a PSO [2,10].

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“…Evolution strategies (ES) developed by Rechenberg [44,45] and Schwefel [52], have been traditionally used for optimization problems with real-valued vector representations. As Genetic Algorithms, GA, [21], and the ES are heuristic search techniques based on the building blocks hypothesis.…”

Section: Robot Controllermentioning

confidence: 99%

Untitled

Berlanga

Sanchís

Isasi

et al. 2002

Journal of Intelligent and Robotic Systems

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Abstract. In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.

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Evolution Strategy: Nature’s Way of Optimization

Cited by 109 publications

References 1 publication

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

Exploratory Toolkit for Evolutionary and Swarm-Based Optimization

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