This paper builds the normal model of fitness sharing with proportionate selection on real-valued functions, and derives the dynamic formula to describe the evolution process of the population with the fitness sharing...
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This paper builds the normal model of fitness sharing with proportionate selection on real-valued functions, and derives the dynamic formula to describe the evolution process of the population with the fitness sharing. The normal modeling simulation is investigated on specific test functions, and experimental results illustrate that the normal model is able to describe exactly the dynamics of the fitness sharing EAs and is a good platform to study the behavior of the fitness sharing EAs with regard to niching radius. The experimental results of the normal modeling simulation and the fitness sharing EAs verify the dilemma in finding optimal niche radius to achieve both good niching convergence and niching efficiency, for which a hybrid scheme is proposed to carry out the niching task. (C) 2009 Elsevier B. V. All rights reserved.
Constraint handling is not straightforward in evolutionary algorithms (EAs) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant,...
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Constraint handling is not straightforward in evolutionary algorithms (EAs) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade, numerous EAs for solving constraint satisfaction problems (CSP) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these EAs on a systematically generated test suite of random binary CSPs. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the evolutionary computing field.
In this paper, mixed-integer hybrid differential evolution (MIHDE) is developed to deal with the mixed-integer optimization problems. This hybrid algorithm contains the migration operation to avoid candidate individua...
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In this paper, mixed-integer hybrid differential evolution (MIHDE) is developed to deal with the mixed-integer optimization problems. This hybrid algorithm contains the migration operation to avoid candidate individuals clustering together. We introduce the population diversity measure to inspect when the migration operation should be performed so that the user can use a smaller population size to obtain a global solution. A mixed coding representation and a rounding operation are introduced in MIHDE so that the hybrid algorithm is not only used to solve the mixed-integer nonlinear optimization problems, but also used to solve the real and integer nonlinear optimization problems. Some numerical examples are tested to illustrate the performance of the proposed algorithm. Numerical examples show that the proposed algorithm converges to better solutions than the conventional genetic algorithms. (C) 2004 Elsevier Ltd. All rights reserved.
Context evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many specific aspects of these algorithms have been evaluated in detail (e.g., test len...
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Context evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many specific aspects of these algorithms have been evaluated in detail (e.g., test length and different kinds of techniques aimed at improving performance, like seeding), the influence of the choice of evolutionary algorithm has to date seen less attention in the literature. Objective: Since it is theoretically impossible to design an algorithm that is the best on all possible problems, a common approach in software engineering problems is to first try the most common algorithm, a genetic algorithm, and only afterwards try to refine it or compare it with other algorithms to see if any of them is more suited for the addressed problem. The objective of this paper is to perform this analysis, in order to shed light on the influence of the search algorithm applied for unit test generation. Method: We empirically evaluate thirteen different evolutionary algorithms and two random approaches on a selection of non-trivial open source classes. All algorithms are implemented in the Evosuite test generation tool, which includes recent optimisations such as the use of an archive during the search and optimisation for multiple coverage criteria. Results: Our study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it confirms that the DynaMOSA many-objective search algorithm is the most effective algorithm for unit test generation. Conclusion: Our results show that the choice of algorithm can have a substantial influence on the performance of whole test suite optimisation. Although we can make a recommendation on which algorithm to use in practice, no algorithm is clearly superior in all cases, suggesting future work on improved search algorithms for unit test generation.
This article presents an empirical study devoted to characterize the computational efficiency behavior of an evolutionary algorithm (usually called canonical) as a C program. The study analyzes the effects of several ...
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This article presents an empirical study devoted to characterize the computational efficiency behavior of an evolutionary algorithm (usually called canonical) as a C program. The study analyzes the effects of several implementation decisions on the execution time of the resulting evolutionary algorithm. The implementation decisions studied include: memory utilization (using dynamic vs. static variables and local vs. global variables), methods for ordering the population, code substitution mechanisms, and the routines for generating pseudorandom numbers within the evolutionary algorithm. The results obtained in the experimental analysis allow us to conclude that significant improvements in efficiency can be gained by applying simple guidelines to best program an evolutionary algorithm in C. Copyright (C) 2013 John Wiley & Sons, Ltd.
In this work, the planning of secondary distribution circuits is approached as a mixed integer nonlinear programming problem (MINLP). In order to solve this problem, a dedicated evolutionary algorithm (EA) is proposed...
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In this work, the planning of secondary distribution circuits is approached as a mixed integer nonlinear programming problem (MINLP). In order to solve this problem, a dedicated evolutionary algorithm (EA) is proposed. This algorithm uses a codification scheme, genetic operators, and control parameters, projected and managed to consider the specific characteristics of the secondary network planning. The codification scheme maps the possible solutions that satisfy the requirements in order to obtain an effective and low-cost projected system-the conductors' adequate dimensioning, load balancing among phases, and the transformer placed at the center of the secondary system loads. An effective algorithm for three-phase power flow is used as an auxiliary methodology of the EA for the calculation of the fitness function proposed for solutions of each topology. Results for two secondary distribution circuits are presented, whereas one presents radial topology and the other a weakly meshed topology.
We have developed a steady-state elitist evolutionary algorithm to approximate the Pareto-optimal frontiers of multiobjective decision making problems. The algorithms define a territory around each individual to preve...
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We have developed a steady-state elitist evolutionary algorithm to approximate the Pareto-optimal frontiers of multiobjective decision making problems. The algorithms define a territory around each individual to prevent crowding in any region. This maintains diversity while facilitating the fast execution of the algorithm. We conducted extensive experiments on a variety of test problems and demonstrated that our algorithm performs well against the leading multiobjective evolutionary algorithms. We also developed a mechanism to incorporate preference information in order to focus on the regions that are appealing to the decision maker. Our experiments show that the algorithm approximates the Pareto-optimal solutions in the desired region very well when we incorporate the preference information.
The inverse modeling of heat transfer is a useful tool in analyzing contact heat transfer at the ingot surfaces during the continuous casting process. The determination of the boundary conditions involves an experimen...
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The inverse modeling of heat transfer is a useful tool in analyzing contact heat transfer at the ingot surfaces during the continuous casting process. The determination of the boundary conditions involves an experimental work consisting in the evaluation of the thermal history, generally at the casting surface, experimentally provided by infrared pyrometers. Additionally, numerical simulations, based on the solution of the 2D transient heat conduction equation, are performed in order to be inversely solved in response to the measured thermal data furnished by the sensor. Due to computational time consumption during simulations in searching cooling conditions, this work proposes an interaction between natural inspired algorithms, called evolutionary algorithms, and the numerical model in order to speed up the searching process. The present work aims to compare three algorithms, namely genetic algorithm, improved stochastic ranking evolutionary strategy, and evolutionary strategy with Cauchy distribution. The latter develops a metaheuristic version of an evolutionary strategy workflow, using a Cauchy random number function to generate each individual, instead of the usual uniform distribution function available in almost all programming languages. The surface temperature, solid shell, and molten pool profiles from the determined cooling conditions are analyzed in terms of casting quality.
This study aimed to develop a new approach to build a functioning groundwater monitoring system by detecting a reduced set of observation wells (OWs) that optimally matches the hydraulic heads measured by other OWs wi...
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This study aimed to develop a new approach to build a functioning groundwater monitoring system by detecting a reduced set of observation wells (OWs) that optimally matches the hydraulic heads measured by other OWs within the field, namely as leader wells (LWs). The optimization models used in this work are the well-known genetic algorithm (GA) and modified genetic algorithm (MGA) and a new progressive combination (PC) model. Optimization was applied to achieve three sequential selection processes: best input combinations (BICk), LWs and core leader wells (CLWs). This approach was applied to the Assiut New Barrage (ANB), a megaproject located in Assiut city, Egypt. The results show that nine LWs among 33 OWs are adequate for regular monitoring, with a reduction ratio of 72.72%. Moreover, assigning CLWs among LWs increases the accuracy of fitting to existing OWs, and helps in understanding the spatial relationships among OWs.
There is no doubt that both determining theoretical properties and characterizing the observed behavior of an evolutionary algorithm allow us to understand when to use such an algorithm in solving a class of optimizat...
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There is no doubt that both determining theoretical properties and characterizing the observed behavior of an evolutionary algorithm allow us to understand when to use such an algorithm in solving a class of optimization problems. One of those evolutionary algorithms is the Hybrid Adaptive evolutionary Algorithm (haea). The general scheme followed by a haea algorithm is to evolve every individual of the population by selecting genetic operators according to a kind of chaotic competition mechanism. This paper proposes and studies, from both theoretical and experimental points of view, the class of hybrid adaptive evolutionary algorithms (called chavela), i.e., the class of evolutionary algorithms that follow such a general scheme. In this way, this paper presents a formal characterization of the chavela class in terms of Markov kernels;establishes convergence properties;proves that (parallel) hill-climbing algorithms belong to the chavela class;develops generational, steady-state, and classic versions;and analyzes the running behavior of chavela on well-known optimization functions.
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