Appendix C: Biologically Inspired Optimization (original) (raw)
Related papers
Taxonomy of bio-inspired optimization algorithms
2019
Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, …. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques. This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied
Computational algorithms inspired by biological processes and evolution
In recent times computational algorithms inspired by biological processes and evolution are gaining much popularity for solving science and engineering problems. These algorithms are broadly classified into evolutionary computation and swarm intelligence algorithms, which are derived based on the analogy of natural evolution and biological activities. These include genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization, artificial neural networks, etc. The algorithms being random-search techniques, use some heuristics to guide the search towards optimal solution and speed-up the convergence to obtain the global optimal solutions. The bio-inspired methods have several attractive features and advantages compared to conventional optimization solvers. They also facilitate the advantage of simulation and optimization environment simultaneously to solve hard-to-define (in simple expressions), real-world problems. These biologically inspired methods have provided novel ways of problem-solving for practical problems in traffic routing, networking, games, industry, robotics, economics, mechanical, chemical, electrical, civil, water resources and others fields. This article discusses the key features and development of bio-inspired computational algorithms, and their scope for application in science and engineering fields.
A Survey : Evolutionary and Swarm Based Bio-Inspired Optimization Algorithms
2012
Nature is the best tutor and its designs and strengths are extremely massive and strange that it gives inspiration to researches to imitate nature to solve hard and complex problems in computer sciences. Bio Inspired computing has come up as a new era in computation covering wide range of applications. This paper gives overview of most predominant and successful classes of bio inspired optimization methods involving evolutionary and swarm based algorithms inspired by natural evolution and collective behavior in animals respectively.
Evolutionary Biology, 2019
Genetic Algorithms and Evolutionary Computation publishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implementation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). Proposals in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy GENAGENAGENA systems, and quantum computing will be considered for GENAGENAGENA publication in this series as long as GEC techniques are part of Genetic Algorithms and or inspiration for the system being described. Manuscripts Evolutionary Computation describing GEC applications in all areas of engineering, commerce, the sciences, and the humanities are encouraged.
Special issue on evolutionary fuzzy systems
Soft Computing-A Fusion of Foundations, Methodologies and Applications, 2011
Since the early 1990s, evolutionary algorithms have been utilized to optimize and learn fuzzy rule-based systems (Cordón et al. 2001a, 2004a; Herrera 2005, 2008; http://sci2s. ugr. es/gfs/). This hybridization is often called evolutionary fuzzy systems (EFSs) or genetic fuzzy systems (GFSs). A list of journal special issues (Herrera 1997; Herrera and Magdalena 1998; Cordón et al. 2001b, 2004b, 2007; Casillas et al. 2007; Carse and Pipe 2007; Casillas and Carse 2009; Alcalá and Nojima 2009) together with a large number of papers related ...
An Ecosystemic View For Developing Biologically Plausible Optimization Systems
PhD Thesis, 2013
Esta tese foi apresentada como requisito parcial à obtenção do título de Doutor em CIÊNCIAS ABSTRACT PARPINELLI, R. S.. AN ECOSYSTEMIC VIEW FOR DEVELOPING BIOLOGICALLY PLAUSIBLE OPTIMIZATION SYSTEMS. 128 f. Thesis -Electrical and Computer Engineering Graduate Program, Federal University of Technology -Paraná. Curitiba, 2013.
Current Issues and Future Directions in Evolutionary Fuzzy Systems Research
Abstract Contributors to the special track on Evolutionary Fuzzy Systems at the EUSFLAT 2003 conference were asked to record their thoughts and ideas on the current state of evolutionary fuzzy systems research," burning issues" and future directions. This paper brings together these contributions. Keywords: Evolutionary Algorithms. Fuzzy Systems.
Evolutionary and Bio-Inspired Computation: Theory and Applications IV
2010
The papers included in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. Some conference presentations may not be available for publication. The papers published in these proceedings reflect the work and thoughts of the authors and are published herein as submitted. The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.