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187
DEPSO: Hybrid Particle Swarm with Differential Evolution Operator
, 2003
"... A hybrid particle swarm with differential evolution operator, termed DEPSO, which provide the bell-shaped mutations with consensus on the population diversity along with the evolution, while keeps the self-organized particle swarm dynamics, is proposed. Then it is applied to a set of benchmark funct ..."
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Cited by 63 (3 self)
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A hybrid particle swarm with differential evolution operator, termed DEPSO, which provide the bell-shaped mutations with consensus on the population diversity along with the evolution, while keeps the self-organized particle swarm dynamics, is proposed. Then it is applied to a set of benchmark functions, and the experimental results illustrate its efficiency.
Evolutionary computation in structural design
- Journal of Engineering with Computers
, 2001
"... Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technolog ..."
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Cited by 54 (7 self)
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Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems ’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.
A Framework for QoS-Aware Binding and Re-Binding of Composite Web Services
"... QoS-aware dynamic binding of composite services provides the capability of binding each service invocation in a composition to a service chosen among a set of functionally equivalent ones to achieve a QoS goal, for example minimizing the response time while limiting the price under a maximum value. ..."
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Cited by 53 (2 self)
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QoS-aware dynamic binding of composite services provides the capability of binding each service invocation in a composition to a service chosen among a set of functionally equivalent ones to achieve a QoS goal, for example minimizing the response time while limiting the price under a maximum value. This paper proposes a QoS-aware binding approach based on Genetic Algorithms. The approach includes a feature for early run-time re-binding whenever the actual QoS deviates from initial estimates, or when a service is not available. The approach has been implemented in a framework and empirically assessed through two different service compositions.
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection,
- Advanced Engineering Informatics
, 2002
"... In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniqu ..."
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Cited by 38 (2 self)
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In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniques, it does not require niching (or any other similar approach) to maintain diversity in the population. We validated the algorithm using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach is a viable alternative to the traditional penalty function, mainly in engineering optimization problems.
Penalty function methods for constrained optimization with genetic algorithms
- Mathematical and Computational Applications
, 2005
"... Abstract- Genetic Algorithms are most directly suited to unconstrained optimization. Application of Genetic Algorithms to constrained optimization problems is often a challenging effort. Several methods have been proposed for handling constraints. The most common method in Genetic Algorithms to hand ..."
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Cited by 34 (0 self)
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Abstract- Genetic Algorithms are most directly suited to unconstrained optimization. Application of Genetic Algorithms to constrained optimization problems is often a challenging effort. Several methods have been proposed for handling constraints. The most common method in Genetic Algorithms to handle constraints is to use penalty functions. In this paper, we present these penalty-based methods and discuss their strengths and weaknesses.
Search biases in constrained evolutionary optimization
- IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART C—APPLICATIONS AND REVIEWS
, 2005
"... A common approach to constraint handling in evolutionary optimization is to apply a penalty function to bias the search towards a feasible solution. It has been proposed that the subjective setting of various penalty parameters can be avoided using a multi-objective formulation. This paper analyse ..."
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Cited by 33 (3 self)
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A common approach to constraint handling in evolutionary optimization is to apply a penalty function to bias the search towards a feasible solution. It has been proposed that the subjective setting of various penalty parameters can be avoided using a multi-objective formulation. This paper analyses and explains in depth why and when the multi-objective approach to constraint handling is expected to work or fail. Furthermore, an improved evolutionary algorithm based on evolution strategies and differential variation is proposed. Extensive experimental studies have been carried out. Our results reveal that the unbiased multi-objective approach to constraint handling may not be as effective as one may have assumed.
A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems
- IEEE Transactions on Evolutionary computation
, 2003
"... This paper presents a simple multimembered evolution strategy (SMES) to solve global nonlinear optimization problems. The approach does not require the use of a penalty function and it does not require any extra parameters (besides those used with an evolution strategy). Instead, it uses a simple di ..."
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Cited by 32 (5 self)
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This paper presents a simple multimembered evolution strategy (SMES) to solve global nonlinear optimization problems. The approach does not require the use of a penalty function and it does not require any extra parameters (besides those used with an evolution strategy). Instead, it uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population This technique helps the algorithm to find the global optimum despite reaching reasonably fast the feasible region of the search space. Some simple selection criteria are used to guide the process to the feasible region of the search space. Also, the initial step size of the evolution strategy is reduced in order to perform a finer search and a combined (discrete/intermediate) recombination technique improves its exploitation capabilities. The approach was tested with a well-known benchmark. The results obtained are very competitive, when comparing the proposed approach against other state-of-the art techniques and its computational cost (measured by the number of fitness function evaluations) is lower than the required cost of the other techniques compared. 1
Towards a generic framework for automated video game level creation. Applications of Evolutionary Computation
, 2010
"... Abstract. This paper presents a generative system for the automatic creation of video game levels. Our approach is novel in that it allows high-level design goals to be expressed in a top-down manner, while existing bottom-up techniques do not. We use the FI-2Pop genetic algorithm as a natural way t ..."
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Cited by 31 (1 self)
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Abstract. This paper presents a generative system for the automatic creation of video game levels. Our approach is novel in that it allows high-level design goals to be expressed in a top-down manner, while existing bottom-up techniques do not. We use the FI-2Pop genetic algorithm as a natural way to express both con-straints and optimization goals for potential level designs. We develop a genetic encoding technique specific to level design, which proves to be extremely flexi-ble. Example levels are generated for two different genres of game, demonstrating the system’s broad applicability. Key words: video games, level design, procedural content, genetic algorithms 1
Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization
- Journal of Global Optimization
, 2004
"... In this paper, a simulated-annealing-based method called Filter Simulated Annealing (FSA) method is proposed to deal with the constrained global optimization problem. The considered problem is reformulated so as to take the form of optimizing two functions; the objective function and the constrai ..."
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Cited by 27 (5 self)
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In this paper, a simulated-annealing-based method called Filter Simulated Annealing (FSA) method is proposed to deal with the constrained global optimization problem. The considered problem is reformulated so as to take the form of optimizing two functions; the objective function and the constraint violation function. Then, the FSA method is applied to solve the reformulated problem. The FSA method invokes a multi-start diversification scheme in order to achieve an e#cient exploration process.
Introducing a Feasible-Infeasible Two-Population (FI-2Pop) Genetic Algorithm for Constrained Optimization: Distance Tracing and No Free Lunch
, 2005
"... We explore data-driven methods for gaining insight into the dynamics of a two population genetic algorithm (GA), which has been effective for constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solution ..."
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Cited by 24 (3 self)
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We explore data-driven methods for gaining insight into the dynamics of a two population genetic algorithm (GA), which has been effective for constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solutions are selected and bred to improve their objective function values. Infeasible solutions are selected and bred to reduce their constraint violations. Interbreeding between populations is completely indirect, that is, only through their offspring that happen to migrate to the other population. We introduce an empirical measure of distances between individuals and population centroids to monitor the progress of evolution. We find that the centroids of the two populations approach each other and stabilize. This is a valuable characterization of convergence. We find the infeasible population influences, and sometimes dominates the genetic material of the optimum solution. Since the infeasible population is not evaluated by the objective function, it is free to explore boundary regions, where the optimum may be found. This is a blackbox algorithm. Roughly speaking, the No Free Lunch theorems for optimization show that all blackbox algorithms (such as Genetic Algorithms) have the same average performance over the set of all problems. As such, our algorithm would, on average, be no better than random search or any other blackbox search method. However, we provide two general theorems that give conditions that render null the No Free Lunch results. The approach taken here thereby escapes the No Free Lunch implications.
