Expediting the convergence of evolutionary algorithms by identifying promising regions of the search space

2020 ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Ahsanul Habib ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Evolutionary Algorithms ◽  
Search Space

A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms

2010 ◽  
Vol 18 (3) ◽  
pp. 451-489 ◽  
Author(s):  
Tatsuya Motoki
Keyword(s):  
Markov Chain ◽  
Evolutionary Algorithms ◽  
Finite Population ◽  
Fitness Landscape ◽  
Markov Chain Model ◽  
Search Space ◽  
Chain Model ◽  
Transition Matrices ◽  
Memory Space ◽  
Exact Model

As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests.

scholarly journals Evolving Solutions to Community-Structured Satisfiability Formulas

2019 ◽  
Vol 33 ◽  
pp. 2346-2353
Author(s):  
Frank Neumann ◽  
Andrew M. Sutton
Keyword(s):  
Community Structure ◽  
Evolutionary Algorithms ◽  
Objective Function ◽  
Search Space ◽  
Propositional Satisfiability ◽  
Satisfying Assignment ◽  
Distance Correlation ◽  
Strong Signal ◽  
Fitness Distance Correlation ◽  
Correlation Properties

We study the ability of a simple mutation-only evolutionary algorithm to solve propositional satisfiability formulas with inherent community structure. We show that the community structure translates to good fitness-distance correlation properties, which implies that the objective function provides a strong signal in the search space for evolutionary algorithms to locate a satisfying assignment efficiently. We prove that when the formula clusters into communities of size s ∈ ω(logn) ∩O(nε/(2ε+2)) for some constant 0

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

2003 ◽  
Vol 11 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Andrea Toffolo ◽  
Ernesto Benini
Keyword(s):  
Genetic Diversity ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Selection Pressure ◽  
Evaluation Method ◽  
State Of The Art ◽  
Search Space ◽  
Test Problems ◽  
Multi Objective ◽  
Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Improving In-Flight Learning in a Flapping Wing Micro Air Vehicle

2016 ◽  
Vol 4 (1) ◽  
pp. 62-75
Author(s):  
Monica Sam ◽  
Sanjay Boddhu ◽  
Kayleigh Duncan ◽  
Hermanus Botha ◽  
John Gallagher
Keyword(s):  
Evolutionary Algorithms ◽  
Control Problem ◽  
Flight Control ◽  
Search Space ◽  
Flapping Wing ◽  
Micro Air Vehicle ◽  
Flight Trajectory ◽  
Air Vehicle ◽  
Major Criterion ◽  
Sampling Approach

Much effort has gone into improving the performance of evolutionary algorithms that augment traditional control in a Flapping Wing Micro Air Vehicle. An EA applied to such a vehicle in flight is expected to evolve solutions quickly to prevent disruptions in following the desired flight trajectory. Time to evolve solutions therefore is a major criterion by which performance of an algorithm is evaluated. This paper presents results of applying an assortment of different evolutionary algorithms to the problem. This paper also presents some discussion on which choices for representation and algorithm parameters would be optimal for the flight control problem and the rationale behind it. The authors also present a guided sampling approach of the search space to make use of the redundancy of workable solutions found in the search space. This approach has been demonstrated to improve learning times when applied to the problem.

scholarly journals Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization

1999 ◽  
Vol 7 (1) ◽  
pp. 19-44 ◽  
Author(s):  
Slawomir Koziel ◽  
Zbigniew Michalewicz
Keyword(s):  
Evolutionary Algorithms ◽  
Parameter Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Space ◽  
Test Cases ◽  
Nonlinear Constraints ◽  
New Approach ◽  
Survey Papers ◽  
Dimensional Cube

During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms (EAs) for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids. In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n-dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.

scholarly journals Evolutionary Search with Multiple Utopian Reference Points in Decomposition-Based Multiobjective Optimization

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Wu Lin ◽  
Qiuzhen Lin ◽  
Zexuan Zhu ◽  
Jianqiang Li ◽  
Jianyong Chen ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Reference Point ◽  
Ideal Point ◽  
Search Space ◽  
Weight Vector ◽  
Reference Points ◽  
Test Problems ◽  
Evolutionary Search ◽  
Multiobjective Evolutionary Algorithms ◽  
Nadir Point

Decomposition-based multiobjective evolutionary algorithms (MOEA/Ds) have become increasingly popular in recent years. In these MOEA/Ds, evolutionary search is guided by the used weight vectors in decomposition function to approximate the Pareto front (PF). Generally, the decomposition function will be constructed by the weight vectors and the reference point, which play an important role to balance convergence and diversity during the evolutionary search. However, in most existing MOEA/Ds, only one ideal point is used as the reference point for all the evolutionary search, which is harmful to search the entire PF when tackling the problems with difficult-to-approximate PF boundaries. To address the above problem, this paper proposes an evolutionary search method with multiple utopian reference points in MOEA/Ds. Similar to the existing MOEA/Ds, each solution is associated with one weight vector, which provides an evolutionary search direction, while the novelty of our approach is to use multiple utopian reference points, which can provide evolutionary search directions for different weight vectors. Corner solutions are used to approximate the nadir point and then multiple utopian reference points for evolutionary search can be constructed based on the ideal point and the nadir point, which are uniformly distributed on the coordinate axis or planes. The use of these utopian points can prevent solutions to gather in the same region of PF and helps to strike a good balance of exploration and exploitation in the search space. The performance of our proposed algorithm is validated on tackling 16 recently proposed test problems with difficult-to-approximate PF boundaries and empirically compared to eight state-of-the-art multiobjective evolutionary algorithms. The experimental results demonstrate the superiority of the proposed algorithm on solving most of the test problems adopted.

Nelder-Mead Evolutionary Hybrid Algorithms

2011 ◽  
pp. 1191-1196 ◽  
Author(s):  
Sanjoy Das
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Simplex Algorithm ◽  
Stochastic Methods ◽  
Great Success ◽  
Local Minima ◽  
Swarm Optimization

Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).

A Comparative Study of Evolutionary Algorithms for Maximizing Reliability of a Flow in Cellular IP Network

2012 ◽  
pp. 247-257
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Evolutionary Algorithms ◽  
Comparative Study ◽  
Rapid Development ◽  
Experimental Studies ◽  
Access Network ◽  
Search Space ◽  
Ip Network ◽  
Cellular Ip ◽  
Reliability Issue ◽  
Characteristic Measure

The rapid development in technology, witnessed in daily communication, especially in wireless communication, is a good motivation for performance improvement in this field. Cellular IP access network is a suitable environment where a micro mobility of mobile users is implemented and managed. The reliability of Cellular IP network during the communication is an important characteristic measure and must be considered while designing a new model. Evolutionary Algorithms are powerful tools for optimization and problem solving, which require extracting the best solution from a big search space. This chapter explores the reliability issue in Cellular IP of a flow of packets passing through the route from a source to a destination. The main aim of the chapter is to maximize the reliability of the flow passing through a route having number of routers. Two Evolutionary Algorithms (EAs), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been used for this purpose, and a comparative study between the two is performed. Experimental studies of the proposed work have also been performed.

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