Evolutionary Computing

2009 ◽  
pp. 131-142
Author(s):  
Thomas E. Potok ◽  
Xiaohui Cui ◽  
Yu Jiao
Keyword(s):  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Swarm Intelligence ◽  
Real World ◽  
Artificial Life ◽  
Evolutionary Computing ◽  
New Paradigm ◽  
New Methods ◽  
Nature Inspired Computing ◽  
Real World Problems

The rate at which information overwhelms humans is significantly more than the rate at which humans have learned to process, analyze, and leverage this information. To overcome this challenge, new methods of computing must be formulated, and scientist and engineers have looked to nature for inspiration in developing these new methods. Consequently, evolutionary computing has emerged as new paradigm for computing, and has rapidly demonstrated its ability to solve real-world problems where traditional techniques have failed. This field of work has now become quite broad and encompasses areas ranging from artificial life to neural networks. This chapter specifically focuses on two sub-areas of nature-inspired computing: Evolutionary Algorithms and Swarm Intelligence.

EDITORIAL

2002 ◽  
Vol 16 (07) ◽  
pp. 773-779 ◽  
Author(s):  
NIKHIL R. PAL ◽  
SRIMANTA PAL
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Computational Intelligence ◽  
Rough Sets ◽  
Artificial Life ◽  
Considerable Potential ◽  
Intelligence System ◽  
Intelligent Behavior ◽  
Real World Problems

Irrespective of the way computational intelligence (CI) is defined, its components should have the following characteristics: considerable potential in solving real world problems, ability to learn from experience, capability of self-organizing, and ability of adapting in response to dynamically changing conditions and constraints. To summarize, it should display aspects of intelligent behavior as observed in humans. In view of these, we assume that the major ingredients of a computational intelligence system are artificial neural networks, fuzzy sets, rough sets, and evolutionary computation. Some other components that may be parts of computational intelligence (CI) systems are artificial life and immuno computing. It is a synergistic combination of all these components.

Swarm Intelligence for Electromagnetic Problem Solving

2017 ◽  
pp. 69-100 ◽  
Author(s):  
Luciano Mescia ◽  
Pietro Bia ◽  
Diego Caratelli ◽  
Johan Gielis
Keyword(s):  
Stochastic Process ◽  
Sensitivity Analysis ◽  
Problem Solving ◽  
Swarm Intelligence ◽  
Real World ◽  
Computational Burden ◽  
Electromagnetic Problems ◽  
Definition Of ◽  
Quantum Pso ◽  
Real World Problems

The chapter will describe the potential of the swarm intelligence and in particular quantum PSO-based algorithm, to solve complicated electromagnetic problems. This task is accomplished through addressing the design and analysis challenges of some key real-world problems. A detailed definition of the conventional PSO and its quantum-inspired version are presented and compared in terms of accuracy and computational burden. Some theoretical discussions concerning the convergence issues and a sensitivity analysis on the parameters influencing the stochastic process are reported.

Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting

2012 ◽  
Vol 20 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Kent McClymont ◽  
Ed Keedwell
Keyword(s):  
Computational Complexity ◽  
Evolutionary Algorithms ◽  
Real World ◽  
Future Generations ◽  
Nsga Ii ◽  
New Methods ◽  
Research Fields ◽  
Dominance Relationships ◽  
Large Populations ◽  
Novel Methods

In recent years an increasing number of real-world many-dimensional optimisation problems have been identified across the spectrum of research fields. Many popular evolutionary algorithms use non-dominance as a measure for selecting solutions for future generations. The process of sorting populations into non-dominated fronts is usually the controlling order of computational complexity and can be expensive for large populations or for a high number of objectives. This paper presents two novel methods for non-dominated sorting: deductive sort and climbing sort. The two new methods are compared to the fast non-dominated sort of NSGA-II and the non-dominated rank sort of the omni-optimizer. The results demonstrate the improved efficiencies of the deductive sort and the reductions in comparisons that can be made when applying inferred dominance relationships defined in this paper.

A Novel BFO Optimization Algorithm with Neighborhood Learning

2014 ◽  
Vol 543-547 ◽  
pp. 1888-1891
Author(s):  
Li Jing Tan ◽  
Fu Yong Lin ◽  
Ben Niu ◽  
Qi Qi Duan ◽  
Kai Yin
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Real World ◽  
Convergence Speed ◽  
Local Minima ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Simulation Results ◽  
Swarm Intelligence Algorithm ◽  
Real World Problems

Bacterial foraging optimization is a relatively new bio-inspired swarm intelligence algorithm and has been successfully applied to solve many real-world problems. However, similar to other swarm intelligence algorithms, BFO also faces up to some challenging problems, such as low convergence speed and easily to be trapped into local minima. To deal with these issues, we incorporate the concept of neighbor topology and the idea of neighbor learning to improve the performance of BFO, called bacterial foraging optimization with neighborhood learning (BFO-NL). Simulation results demonstrated the good performance of our proposed BFO-NL when compared with original BFO.

scholarly journals Visualisation of the combinatorial effects within evolutionary algorithms: the compass plot

2020 ◽  
Author(s):  
Qi Wang ◽  
Miaoting Guan ◽  
Wen Huang ◽  
Libing Wang ◽  
Zhihong Wang ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Real World ◽  
Distribution System ◽  
Water Distribution ◽  
Cost Effective ◽  
Considerable Change ◽  
Nsga Ii ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Real World Problems

Abstract Applications of evolutionary algorithms (EAs) to real-world problems are usually hindered due to parameterisation issues and computational efficiency. This paper shows how the combinatorial effects related to the parameterisation issues of EAs can be visualised and extracted by the so-called compass plot. This new plot is inspired by the traditional Chinese compass used for navigation and geomantic detection. We demonstrate the value of the proposed compass plot in two scenarios with application to the optimal design of the Hanoi water distribution system. One is to identify the dominant parameters in the well-known NSGA-II. The other is to seek the efficient combinations of search operators embedded in Borg, which uses an ensemble of search operators by auto-adapting their use at runtime to fit an optimisation problem. As such, the implicit and vital interdependency among parameters and search operators can be intuitively demonstrated and identified. In particular, the compass plot revealed some counter-intuitive relationships among the algorithm parameters that led to a considerable change in performance. The information extracted, in turn, facilitates a deeper understanding of EAs and better practices for real-world cases, which eventually leads to more cost-effective decision-making.

scholarly journals An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 116
Author(s):  
Junhua Ku ◽  
Fei Ming ◽  
Wenyin Gong
Keyword(s):  
Evolutionary Algorithms ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Multi Objective Optimization ◽  
The Real ◽  
Multi Objective ◽  
The Past ◽  
Hypervolume Indicator ◽  
Real World Problems

In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances.

Development of ANN with Adaptive Connections by CE

2006 ◽  
pp. 71-93
Author(s):  
Julián Dorado ◽  
Nieves Pedreira ◽  
Mónica Miguelez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real World ◽  
Development Process ◽  
Multilevel System ◽  
Processing Elements ◽  
Artificial Neural ◽  
Real World Problems

This chapter presents the use of Artificial Neural Networks (ANN) and Evolutionary Computation (EC) techniques to solve real-world problems including those with a temporal component. The development of the ANN maintains some problems from the beginning of the ANN field that can be palliated applying EC to the development of ANN. In this chapter, we propose a multilevel system, based on each level in EC, to adjust the architecture and to train ANNs. Finally, the proposed system offers the possibility of adding new characteristics to the processing elements (PE) of the ANN without modifying the development process. This characteristic makes possible a faster convergence between natural and artificial neural networks.

Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks

2006 ◽  
pp. 28-42
Author(s):  
Ruhul A. Sarker ◽  
Hussein A. Abbass
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiobjective Optimization ◽  
Real World ◽  
Network Architectures ◽  
Research Areas ◽  
Artificial Neural ◽  
Multiobjective Algorithms ◽  
Simultaneous Evolution ◽  
Real World Problems

Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the latest research areas in this field is evolving ANNs. In this chapter, we investigate the simultaneous evolution of network architectures and connection weights in ANNs. In simultaneous evolution, we use the well-known concept of multiobjective optimization and subsequently evolutionary multiobjective algorithms to evolve ANNs. The results are promising when compared with the traditional ANN algorithms. It is expected that this methodology would provide better solutions to many applications of ANNs.

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