Evolutionary Computation, IEEE Transactions on - new TOC

Feb. 2012

IEEE Transactions on Evolutionary Computation publication information

Feb. 2012

Robustness Against the Decision-Maker's Attitude to Risk in Problems With Conflicting Objectives

Feb. 2012

In multiobjective optimization problems (MOPs), the Pareto set consists of efficient solutions that represent the best trade-offs between the conflicting objectives. Many forms of uncertainty affect the MOP, including uncertainty in the decision variables, parameters or objectives. A source of uncertainty that is not studied in the evolutionary multiobjective optimization (EMO) literature is the decision-maker's attitude to risk (DMAR) even though it has great significance in real-world applications. Often the decision-makers change over the course of the decision-making process and thus, some relevant information about preferences of future decision-makers is unknown at the time a decision is made. This poses a major risk to organizations because a new decision-maker may simply reject a decision that has been made previously. When an EMO technique attempts to generate the set of nondominated solutions for a problem, then DMAR-related uncertainty needs to be reduced. Solutions generated by an EMO technique should be robust against perturbations caused by the DMAR. In this paper, we focus on the DMAR as a source of uncertainty and present two new types of robustness in MOP. In the first type, dominance robustness (DR), the robust Pareto solutions are those which, if perturbed, would have a high chance to move to another Pareto solution. In the second type, preference robustness (PR), the robust Pareto solutions are those that are close to each other in configuration space. Dominance robustness captures the ability of a solution to move along the Pareto optimal front under some perturbative variation in the decision space, while PR captures the ability of a solution to produce a smooth transition (in the decision variable space) to its neighbors (defined in the objective space). We propose methods to quantify these robustness concepts, modify existing EMO techniques to capture robustness against the DMAR, and present test problems to examine both DR and PR.

Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

Feb. 2012

Since multiobjective evolutionary algorithms (MOEAs) provide a set of nondominated solutions, decision making of selecting a preferred one out of them is required in real applications. However, there has been some research on MOEA in which the user's preferences are incorporated for this purpose. This paper proposes preference-based solution selection algorithm (PSSA) by which user can select a preferred one out of nondominated solutions obtained by any one of MOEAs. The PSSA, which is a kind of multiple criteria decision making (MCDM) algorithm, represents user's preference to multiple objectives or criteria as a degree of consideration by fuzzy measure and globally evaluates obtained solutions by fuzzy integral. The PSSA is also employed in each and every generation of evolutionary process to propose multiobjective quantum-inspired evolutionary algorithm with preference-based selection (MQEA-PS). To demonstrate the effectiveness of PSSA and MQEA-PS, computer simulations and real experiments on evolutionary multiobjective optimization for the fuzzy path planner of mobile robot are carried out. Computer simulation and experiment results show that the user's preference is properly reflected in the selected solution. Moreover, MQEA-PS shows improved performance for the DTLZ problems and fuzzy path planner optimization problem compared to MQEA with dominance-based selection and other MOEAs like NSGA-II and MOPBIL.

A Novel Immune Clonal Algorithm for MO Problems

Feb. 2012

Research on multiobjective optimization (MO) becomes one of the hot points of intelligent computation. Compared with evolutionary algorithm, the artificial immune system used for solving MO problems (MOPs) has shown many good performances in improving the convergence speed and maintaining the diversity of the antibody population. However, the simple clonal selection computation has some difficulties in handling some more complex MOPs. In this paper, the simple clonal selection strategy is improved and a novel immune clonal algorithm (NICA) is proposed. The improvements in NICA are mainly focus on four aspects. Antibodies in the antibody population are divided into dominated ones and nondominated ones, which is suitable for the characteristic of one multiobjective optimization problem has a series Pareto-optimal solutions.

On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization

Feb. 2012

Algorithms that make use of the gradient, i.e., the direction of maximum improvement, to search for the optimum of a single-objective function have been around for decades. They are commonly accepted to be important and have been applied to tackle single-objective optimization problems in many fields. For multiobjective optimization problems, much less is known about the gradient and its algorithmic use. In this paper, we aim to contribute to the understanding of gradients for numerical, i.e., real-valued, multiobjective optimization. Specifically, we provide an analytical parametric description of the set of all nondominated, i.e., most promising, directions in which a solution can be moved such that the objective values either improve or remain the same. This result completes previous work where this set is described only for one particular case, namely when some of the nondominated directions have positive, i.e., nonimproving, components and the final set can be computed by taking the subset of directions that are all nonpositive. In addition we use our result to assess the utility of using gradient information for multiobjective optimization where the goal is to obtain a Pareto set of solutions that approximates the optimal Pareto set. To this end, we design and consider various multiobjective gradient-based optimization algorithms. One of these algorithms uses the description of the multiobjective gradient provided here. Also, we hybridize an existing multiobjective evolutionary algorithm (MOEA) with the various multiobjective gradient-based optimization algorithms. During optimization, the performance of the gradient-based optimization algorithms is monitored and the available computational resources are redistributed to allow the (currently) most effective algorithm to spend the most resources. We perform an experimental analysis using a few well-known benchmark problems to compare the performance of different optimization methods. The results underline that- the use of a population of solutions that is characteristic of MOEAs is a powerful concept if the goal is to obtain a good Pareto set, i.e., instead of only a single solution. This makes it hard to increase convergence speed in the initial phase using gradient information to improve any single solution. However, in the longer run, the use of gradient information does ultimately allow for better fine-tuning of the results and thus better overall convergence.

Improving Generalization Performance in Co-Evolutionary Learning

Feb. 2012

Recently, the generalization framework in co-evolutionary learning has been theoretically formulated and demonstrated in the context of game-playing. Generalization performance of a strategy (solution) is estimated using a collection of random test strategies (test cases) by taking the average game outcomes, with confidence bounds provided by Chebyshev's theorem. Chebyshev's bounds have the advantage that they hold for any distribution of game outcomes. However, such a distribution-free framework leads to unnecessarily loose confidence bounds. In this paper, we have taken advantage of the near-Gaussian nature of average game outcomes and provided tighter bounds based on parametric testing. This enables us to use small samples of test strategies to guide and improve the co-evolutionary search. We demonstrate our approach in a series of empirical studies involving the iterated prisoner's dilemma (IPD) and the more complex Othello game in a competitive co-evolutionary learning setting. The new approach is shown to improve on the classical co-evolutionary learning in that we obtain increasingly higher generalization performance using relatively small samples of test strategies. This is achieved without large performance fluctuations typical of the classical approach. The new approach also leads to faster co-evolutionary search where we can strictly control the condition (sample sizes) under which the speedup is achieved (not at the cost of weakening precision in the estimates).

A Fast Way of Calculating Exact Hypervolumes

Feb. 2012

We describe a new algorithm WFG for calculating hypervolume exactly. WFG is based on the recently-described observation that the exclusive hypervolume of a point $p$ relative to a set $S$ is equal to the difference between the inclusive hypervolume of $p$ and the hypervolume of $S$ with each point limited by the objective values in $p$. WFG applies this technique iteratively over a set to calculate its hypervolume. Experiments show that WFG is substantially faster (in five or more objectives) than all previously-described algorithms that calculate hypervolume exactly.

A Rule-Based Evolutionary Approach to Music Performance Modeling

Feb. 2012

We describe an evolutionary approach to one of the most challenging problems in computer music: modeling how skilled musicians manipulate sound properties such as timing and amplitude in order to express their view of the emotional content of musical pieces. Starting with a collection of audio recordings of real performances, we apply a sequential-covering genetic algorithm in order to obtain computational models for different aspects of expressive performance. We use these models to automatically synthesize performances with the timing and energy expressiveness that characterizes the music generated by a professional musician. The reported results indicate that evolutionary computation is an appropriate technique for solving the problem considered. Specifically, our evolutionary algorithm provides a number of potential advantages over other supervised learning algorithms, such as a method for non-deterministically obtaining models capturing different possible interpretations of a musical piece.

Edge Orientation and the Design of Problem-Specific Crossover Operators for the OCST Problem

Feb. 2012

In the Euclidean optimal communication spanning tree problem, the edges in optimal trees not only have small weights but also point with high probability toward the center of the graph. These characteristics of optimal solutions can be used for the design of problem-specific evolutionary algorithms (EAs). Recombination operators of direct encodings like edge-set and NetDir can be extended such that they prefer not only edges with small distance weights but also edges that point toward the center of the graph. Experimental results show higher performance and robustness in comparison to EAs using existing crossover strategies.

Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems

Feb. 2012

During the past decade, solving constrained optimization problems with evolutionary algorithms has received considerable attention among researchers and practitioners. Cai and Wang's method (abbreviated as CW method) is a recent constrained optimization evolutionary algorithm proposed by the authors. However, its main shortcoming is that a trial-and-error process has to be used to choose suitable parameters. To overcome the above shortcoming, this paper proposes an improved version of the CW method, called CMODE, which combines multiobjective optimization with differential evolution to deal with constrained optimization problems. Like its predecessor CW, the comparison of individuals in CMODE is also based on multiobjective optimization. In CMODE, however, differential evolution serves as the search engine. In addition, a novel infeasible solution replacement mechanism based on multiobjective optimization is proposed, with the purpose of guiding the population toward promising solutions and the feasible region simultaneously. The performance of CMODE is evaluated on 24 benchmark test functions. It is shown empirically that CMODE is capable of producing highly competitive results compared with some other state-of-the-art approaches in the community of constrained evolutionary optimization.

Performance Evaluation of Evolutionary Algorithms for Optimal Filter Design

Feb. 2012

In analog filter design, component values are selected due to manufactured constant values where performing an exhaustive search on all possible combinations of preferred values for obtaining an optimized design is not feasible. The application of evolutionary algorithms (EA) in analog active filter circuit design and optimization is a promising area which is based on concepts of natural selection and survival of the fittest. In this paper, the performances of genetic algorithm, artificial bee colony optimization, and particle swarm optimization, which are nature-inspired EA techniques, are evaluated for active filter design. Each algorithm is applied to two different filter structures and performances of them are also evaluated when filter design is realized with components selected from different manufactured series.

2012 IEEE world congress on computational intelligence

Feb. 2012

IEEE Computational Intelligence Society Information

Feb. 2012

IEEE Transactions on Evolutionary Computation information for authors

Feb. 2012

Evolutionary Computation, IEEE Transactions on - new TOC

Table of contents

IEEE Transactions on Evolutionary Computation publication information

Robustness Against the Decision-Maker's Attitude to Risk in Problems With Conflicting Objectives

Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

A Novel Immune Clonal Algorithm for MO Problems

On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization

Improving Generalization Performance in Co-Evolutionary Learning

A Fast Way of Calculating Exact Hypervolumes

A Rule-Based Evolutionary Approach to Music Performance Modeling

Edge Orientation and the Design of Problem-Specific Crossover Operators for the OCST Problem

Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems

Performance Evaluation of Evolutionary Algorithms for Optimal Filter Design

2012 IEEE world congress on computational intelligence

IEEE Computational Intelligence Society Information

IEEE Transactions on Evolutionary Computation information for authors