Design Of Evolutionary Algorithms Research Articles

Study on the evolutionary optimisation of the topology of network control systems

Computer networks have been very popular in enterprise applications. However, optimisation of network designs that allows networks to be used more efficiently in industrial environment and enterprise applications remains an interesting research topic. This article mainly discusses the topology optimisation theory and methods of the network control system based on switched Ethernet in an industrial context. Factors that affect the real-time performance of the industrial control network are presented in detail, and optimisation criteria with their internal relations are analysed. After the definition of performance parameters, the normalised indices for the evaluation of the topology optimisation are proposed. The topology optimisation problem is formulated as a multi-objective optimisation problem and the evolutionary algorithm is applied to solve it. Special communication characteristics of the industrial control network are considered in the optimisation process. In respect to the evolutionary algorithm design, an improved arena algorithm is proposed for the construction of the non-dominated set of the population. In addition, for the evaluation of individuals, the integrated use of the dominative relation method and the objective function combination method, for reducing the computational cost of the algorithm, are given. Simulation tests show that the performance of the proposed algorithm is preferable and superior compared to other algorithms. The final solution greatly improves the following indices: traffic localisation, traffic balance and utilisation rate balance of switches. In addition, a new performance index with its estimation process is proposed.

MOEA based design of decentralized controllers for LFC of interconnected power systems with nonlinearities, AC–DC parallel tie-lines and SMES units

A new design of Multi-Objective Evolutionary Algorithm based decentralized controllers for load–frequency control of interconnected power systems with Governor Dead Band and Generation Rate Constraint nonlinearities, AC–DC parallel tie-lines and Superconducting Magnetic Energy Storage (SMES) units, is proposed in this paper. The HVDC link is used as system interconnection in parallel with AC tie-line to effectively damp the frequency oscillations of AC system while the SMES unit provides bulk energy storage and release, thereby achieving combined benefits. The proposed controller satisfies two main objectives, namely, minimum Integral Squared Error of the system output and maximum closed-loop stability of the system. Simulation studies are conducted on a two area interconnected power system with nonlinearities, AC–DC tie-lines and SMES units. Results indicate that the proposed controller improves the transient responses and guarantees the closed-loop stability of the overall system even in the presence of system nonlinearities and with parameter changes.

A survey of repair methods used as constraint handling techniques in evolutionary algorithms

This paper provides a survey of the most important repair heuristics used in evolutionary algorithms to solve constrained optimization problems. Popular techniques are reviewed, such as some crossover operators in permutation encoding, algorithms for fixing the number of 1s in binary encoded genetic algorithms, and more specialized techniques such as Hopfield neural networks, heuristics for graphs and trees, and repair heuristics in grouping genetic algorithms. The survey also gives some indications about the design and implementation of hybrid evolutionary algorithms, and provides a revision of the most important applications in which hybrid evolutionary techniques have been used.

Evolutionary design of Evolutionary Algorithms

Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

How Proteins Adapt: Lessons from Directed Evolution

Applying artificial selection to create new proteins has allowed us to explore fundamental processes of molecular evolution. These "directed evolution" experiments have shown that proteins can readily adapt to new functions or environments via simple adaptive walks involving small numbers of mutations. With the entire "fossil record" available for detailed study, these experiments have provided new insight into adaptive mechanisms and the effects of mutation and recombination. Directed evolution has also shown how mutations that are functionally neutral can set the stage for further adaptation. Watching adaptation in real time helps one to appreciate the power of the evolutionary design algorithm.

Analysis and Modelling of Hierarchical Fuzzy Logic Systems

In this article the design and development of a hierarchical fuzzy logic system is investigated. A new method using an evolutionary algorithm for design of hierarchical fuzzy logic system for prediction and modelling of interest rates in Australia is developed. The hierarchical system is developed to model and predict three months (quarterly) interest rate fluctuations. This research study is unique in the way proposed method is applied to design and development of fuzzy logic systems. The new method proposed determines the number of layer for hierarchical fuzzy logic system. The advantages and disadvantages of using fuzzy logic systems for financial modelling is also considered. Conclusions on the accuracy of prediction using hierarchical fuzzy logic systems compared to a back-propagation neural network system and a hierarchical neural network are reported.

A Unified Conceptual Framework for Geographical Optimization Using Evolutionary Algorithms

During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem from being used on others. In this article, a formal, conceptual framework is developed to unify the design and implementation of EAs for many geographical optimization problems. The key element in this framework is a graph representation that defines the spatial structure of a broad range of geographical problems. Based on this representation, four types of geographical optimization problems are discussed and a set of algorithms is developed for problems in each type. These algorithms can be used to support the design and implementation of EAs for geographical optimization. Knowledge specific to geographical optimization problems can also be incorporated into the framework. An example of solving political redistricting problems is used to demonstrate the application of this framework.

Robust Evolutionary Algorithm Design for Socio-Economic Simulation: Some Comments

In the original papers by Alkemade et al. (2006, 2007), their evolutionary algorithms (EAs) exhibited an extreme degree of premature convergence when they were run according to approach I. This seemed a quite curious result. We are happy that the correction (Alkemade et al. 2008) makes clear that the degree of premature convergence is much lower than originally reported. There are three further comments that we would like to make on the work of Alkemade et al. First, Alkemade et al. state that “convergence behavior differs for different values of the EA parameters such as initial density and chromosome length”. We would like to emphasize that the behavior of an EA depends on the initial density parameter only in the short run. In the long run, the initial density parameter has no effect on the behavior of an EA. This is because from amathematical point of view an EA (or, more precisely, a genetic algorithm) is an ergodic Markov chain (e.g., Nix and Vose 1992; Dawid 1996). In the long run, the state of such a Markov chain no longer depends on the initial state. We agree with Alkemade et al. that the behavior of an EA depends on the chromosome length. This is the case not only in the short run but also in the long run. Since the chromosome length is a technical parameter that usually does not have a clear economic interpretation, we regard the dependence of EA behavior on the chromosome length as a disadvantage of the use of EAs for economic modeling. Second, Alkemade et al. point out that two popular approaches to the use of EAs for economic modeling can yield quite different results. This is a very interesting observation, especially since earlier studies usually simply took one of the two approaches

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

This paper characterizes general optimization problems into four categories based on the solution representation schemes, as they have been the key to the design of various evolutionary algorithms (EAs). Four EAs have been designed for different formulations with the aim of utilizing similar and generalized strategies for all of them. Several modifications to the existing EAs have been proposed and studied. First, a new tradeoff function-based mutation has been proposed that takes advantages of Cauchy, Gaussian, random as well as chaotic mutations. In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search. A theoretical analysis has been performed to establish the convergence of the learning rule. A theoretical study has also been performed in order to investigate the various aspects of the search strategy employed by the new tradeoff-based mutations. A more logical parameter tuning has been done by introducing the concept of orthogonal arrays in the EA experimentation. The use of noise-based tuning ensures the robust parameter tuning that enables the EAs to perform remarkably well in the further experimentations. The performance of the proposed EAs has been analyzed for different problems of varying complexities. The results prove the supremacy of the proposed EAs over other well-established strategies given in the literature.

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion. Nowadays, application of design of experiment (DoE), evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein. This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds.

ON SOCIAL LEARNING AND ROBUST EVOLUTIONARY ALGORITHM DESIGN IN THE COURNOT OLIGOPOLY GAME

Agent‐based computational economics (ACE) combines elements from economics and computer science. In this article, the focus is on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters.This article compares two important approaches that are dominating in ACE and shows that the above practice may hinder the performance of the genetic algorithm and thereby hinder agent learning. More specifically, it is shown that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.

Robust Evolutionary Algorithm Design for Socio-economic Simulation

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.

Neutralité dans les paysages de fitness. Évolution artificielle et neutralité

Evolutionary algorithms (EA) are optimization algorithms inspired by the neo-dar winian theory of evolution. Those algorithms use a population of potential solutions which is gradually guided towards better solutions discovered by random variation. The neutral theory of evolution considers that the majority of mutations are selectively neutral or lethal. In this article we present the main results about neutral theory in the context of EA, and particulary how this theory affects studies of fitness landscapes. Fitness landscapes represent the set of potential solutions of a problem as an space equipped with a neighborhood relation and where the solutions have an height corresponding to their performance. This article will show academic and real problems related to neutral landscapes and the nature of EA dynamics on those landscapes. In order to improve the performances of EA, we then present technics based on the choice of redundant coding, just as the design of EA exploiting neutrality.

Ab initio protein fold prediction using evolutionary algorithms: Influence of design and control parameters on performance

True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15-residue polyalanine molecule-design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady-state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady-state designs based on real encoding and multipoint crossover. Application of the steady-state design to met-enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met-enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used.

Rethinking the design of real-coded evolutionary algorithms: Making discrete choices in continuous search domains

Although real-coded evolutionary algorithms (EAs) have been applied to optimization problems for over thirty years, the convergence properties of these methods remain poorly understood. We discuss the use of discrete random variables to perform search in real-valued EAs. Although most real-valued EAs perform mutation with continuous random variables, we argue that EAs using discrete random variables for mutation can be much easier to analyze. In particular, we present and analyze two simple EAs that make discrete choices of mutation steps.

An Evolutionary Design Algorithm for Ring-based SDH optical core networks

This paper presents a ring-based SDH optical core network design problem with multiple constraints. A novel evolutionary algorithm (EA) is developed to successfully find near-optimal solutions for a realistic case study. Compared to a genetic algorithm, this EA is demonstrated to be more efficient and effective by experimental simulation results, and shows promise for tackling ring-based network design problems.

Digital filter design using multiple pareto fronts

Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. One such measure is clustering. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The population is split into a number of clusters, and parent and offspring selection, as well as fitness calculation, are performed on a per-cluster basis. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. They demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.

Locally-Adaptive and Memetic Evolutionary Pattern Search Algorithms

Abstrac Recent convergence analyses of evolutionary pattern search algorithms (EPSAs) have shown that these methods have a weak stationary point convergence theory for a broad class of unconstrained and linearly constrained problems. This paper describes how the convergence theory for EPSAs can be adapted to allow each individual in a population to have its own mutation step length (similar to the design of evolutionary programing and evolution strategies algorithms). These are called locally-adaptive EPSAs (LA-EPSAs) since each individual's mutation step length is independently adapted in different local neighborhoods. The paper also describes a variety of standard formulations of evolutionary algorithms that can be used for LA-EPSAs. Further, it is shown how this convergence theory can be applied to memetic EPSAs, which use local search to re.ne points within each iteration.

Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems

Multiobjective site-search problems are a class of decision problems that have geographical components and multiple, often conflicting, objectives; this kind of problem is often encountered and is technically difficult to solve. In this paper we describe an evolutionary algorithm (EA) based approach that can be used to address such problems. We first describe the general design of EAs that can be used to generate alternatives that are optimal or close to optimal with respect to multiple criteria. Then we define the problem addressed in this research and discuss how the EA was designed to solve it. In this procedure, called MOEA/Site, a solution (that is, a site) is encoded by using a graph representation that is operated on by a set of specifically designed evolutionary operations. This approach is applied to five different types of cost surfaces and the results are compared with 10 000 randomly generated solutions. The results demonstrate the robustness and effectiveness of this EA-based approach to geographical analysis and multiobjective decisionmaking. Critical issues regarding the representation of spatial solutions and associated evolutionary operations are also discussed.

Design of graph-based evolutionary algorithms: a case study for chemical process networks.

This paper describes the adaptation of evolutionary algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures to calculate target function values. To represent chemical engineering plants, a network representation with typed vertices and variable structure will be introduced. For this representation, we introduce a technique on how to create problem specific search operators and apply them in stochastic optimization procedures. The applicability of the approach is demonstrated by a reference example. The design of the algorithms will be oriented at the systematic framework of metric-based evolutionary algorithms (MBEAs). MBEAs are a special class of evolutionary algorithms, fulfilling certain guidelines for the design of search operators, whose benefits have been proven in theory and practice. MBEAs rely upon a suitable definition of a metric on the search space. The definition of a metric for the graph representation will be one of the main issues discussed in this paper. Although this article deals with the problem domain of chemical plant optimization, the algorithmic design can be easily transferred to similar network optimization problems. A useful distance measure for variable dimensionality search spaces is suggested.