IEEE Transactions on Evolutionary Computation information for authors

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An Adaptive Convergence-Trajectory Controlled Ant Colony Optimization Algorithm With Application to Water Distribution System Design Problems

Evolutionary algorithms and other meta-heuristics have been employed widely to solve optimization problems in many different fields over the past few decades. Their performance in finding optimal solutions often depends heavily on the parameterization of the algorithm’s search operators, which affect an algorithm’s balance between search diversification and intensification. While many parameter-adaptive algorithms have been developed to improve the searching ability of meta-heuristics, their performance is often unsatisfactory when applied to real-world problems. This is, at least in part, because available computational budgets are often constrained in such settings due to the long simulation times associated with objective function and/or constraint evaluation, thereby preventing convergence of existing parameter-adaptive algorithms. To this end, this paper proposes an innovative parameter-adaptive strategy for ant colony optimization (ACO) algorithms based on controlling the convergence trajectory in decision space to follow any prespecified path, aimed at finding the best possible solution within a given, and limited, computational budget. The utility of the proposed convergence-trajectory controlled ACO (ACO $_{mathbf{CTC}}$ ) algorithm is demonstrated using six water distribution system design problems (WDSDPs, a difficult type of combinatorial problem in water resources) with varying complexity. The results show that the proposed ACO $_{mathbf{CTC}}$ successfully enables the specified convergence trajectories to be followed by automatically adjusting the algorithm’s parameter values. Different convergence trajectories significantly affect the algorithm’s final performance (solution quality). The trajectory with a slight bias toward diversi-
ication in the first half and more emphasis on intensification during the second half of the search exhibits substantially improved performance compared to the best available ACO variant with the best parameterization (no convergence control) for all WDSDPs and computational scenarios considered. For the two large-scale WDSDPs, new best-known solutions are found by the proposed ACO $_{mathbf{CTC}}$ .

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Member Get-A-Member (MGM) Program

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Autoencoding Evolutionary Search With Learning Across Heterogeneous Problems

To enhance the search performance of evolutionary algorithms, reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and demonstrated much promise. In the literature, there are generally three types of approaches for reusing knowledge from past search experiences, namely exact storage and reuse of past solutions, the reuse of model-based information, and the reuse of structured knowledge captured from past optimized solutions. In this paper, we focus on the third type of knowledge reuse for enhancing evolutionary search. In contrast to existing works, here we focus on knowledge transfer across heterogeneous continuous optimization problems with diverse properties, such as problem dimension, number of objectives, etc., that cannot be handled by existing approaches. In particular, we propose a novel autoencoding evolutionary search paradigm with learning capability across heterogeneous problems. The essential ingredient for learning structured knowledge from search experience in our proposed paradigm is a single layer denoising autoencoder (DA), which is able to build the connections between problem domains by treating past optimized solutions as the corrupted version of the solutions for the newly encountered problem. Further, as the derived DA holds a closed-form solution, the corresponding reusing of knowledge from past search experiences will not bring much additional computational burden on the evolutionary search. To evaluate the proposed search paradigm, comprehensive empirical studies on the complex multiobjective optimization problems are presented, along with a real-world case study from the fiber-reinforced polymer composites manufacturing industry.

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Introducing IEEE Collabratec

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Simplify Your Covariance Matrix Adaptation Evolution Strategy

The standard covariance matrix adaptation evolution strategy (CMA-ES) comprises two evolution paths, one for the learning of the mutation strength and one for the rank-1 update of the covariance matrix. In this paper, it is shown that one can approximately transform this algorithm in such a manner that one of the evolution paths and the covariance matrix itself disappear. That is, the covariance update and the covariance matrix square root operations are no longer needed in this novel so-called matrix adaptation (MA) ES. The MA-ES performs nearly as well as the original CMA-ES. This is shown by empirical investigations considering the evolution dynamics and the empirical expected runtime on a set of standard test functions. Furthermore, it is shown that the MA-ES can be used as a search engine in a bi-population (BiPop) ES. The resulting BiPop-MA-ES is benchmarked using the BBOB comparing continuous optimizers (COCO) framework and compared with the performance of the CMA-ES-v3.61 production code. It is shown that this new BiPop-MA-ES—while algorithmically simpler—performs nearly equally well as the CMA-ES-v3.61 code.

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Bridging the Gap: Many-Objective Optimization and Informed Decision-Making

The field of many-objective optimization has grown out of infancy and a number of contemporary algorithms can deliver well converged and diverse sets of solutions close to the Pareto optimal front. Concurrently, the studies in cognitive science have highlighted the pitfalls of imprecise decision-making in presence of a large number of alternatives. Thus, for effective decision-making, it is important to devise methods to identify a handful (7 ± 2) of solutions from a potentially large set of tradeoff solutions. Existing measures such as reflex/bend angle, expected marginal utility (EMU), maximum convex bulge/distance from hyperplane, hypervolume contribution, and local curvature are inadequate for the purpose as: 1) they may not create complete ordering of the solutions; 2) they cannot deal with large number of objectives and/or solutions; and 3) they typically do not provide any insight on the nature of selected solutions (internal, peripheral, and extremal). In this letter, we introduce a scheme to identify solutions of interest based on recursive use of the EMU measure. The nature of the solutions (internal or peripheral) is then characterized using reference directions generated via systematic sampling and the top ${K}$ solutions with the largest relative EMU measure are presented to the decision maker. The performance of the approach is illustrated using a number of benchmarks and engineering problems. In our opinion, the development of such methods is necessary to bridge the gap between theoretical development and real-world adoption of many-objective optimization algorithms.

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Opposition-Based Memetic Search for the Maximum Diversity Problem

As a usual model for a variety of practical applications, the maximum diversity problem (MDP) is computational challenging. In this paper, we present an opposition-based memetic algorithm (OBMA) for solving MDP, which integrates the concept of opposition-based learning (OBL) into the well-known memetic search framework. OBMA explores both candidate solutions and their opposite solutions during its initialization and evolution processes. Combined with a powerful local optimization procedure and a rank-based quality-and-distance pool updating strategy, OBMA establishes a suitable balance between exploration and exploitation of its search process. Computational results on 80 popular MDP benchmark instances show that the proposed algorithm matches the best-known solutions for most of instances, and finds improved best solutions (new lower bounds) for 22 instances. We provide experimental evidences to highlight the beneficial effect of OBL for solving MDP.

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Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression

When learning from high-dimensional data for symbolic regression (SR), genetic programming (GP) typically could not generalize well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalization ability. However, in GP for high-dimensional SR, feature selection before learning is seldom considered. In this paper, we propose a new feature selection method based on permutation to select features for high-dimensional SR using GP. A set of experiments has been conducted to investigate the performance of the proposed method on the generalization of GP for high-dimensional SR. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability.

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DMOEA- $varepsilon text{C}$ : Decomposition-Based Multiobjective Evolutionary Algorithm With the $varepsilon $ -Constraint Framework

Decomposition is an efficient and prevailing strategy for solving multiobjective optimization problems (MOPs). Its success has been witnessed by the multiobjective evolutionary algorithm MOEA/D and its variants. In decomposition-based methods, an MOP is decomposed into a number of scalar subproblems by using various scalarizing functions. Most decomposition schemes adopt the weighting method to construct scalarizing functions. In this paper, another classical generation method in the field of mathematical programming, that is the $ {varepsilon }$ -constraint method, is adopted for the multiobjective optimization. It selects one of the objectives as the main objective and converts other objectives into constraints. We incorporate the $ {varepsilon }$ -constraint method into the decomposition strategy and propose a new decomposition-based multiobjective evolutionary algorithm with the $ {varepsilon }$ -constraint framework (DMOEA- $ {varepsilon }text{C}$ ). It decomposes an MOP into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound vector. These subproblems are optimized simultaneously by using information from neighboring subproblems. Besides, a main objective alternation strategy, a solution-to-subproblem matching procedure, and a subproblem-to-solution matching procedure are proposed to strike a balance between convergence and diversity. DMOEA- $ {varepsilon }text{C}$ is compared with a number of state-of-the-art multiobjective evolutionary algorithms. Experimental studies demonstrate that DMOEA- $ {-
arepsilon }text{C}$
outperforms or performs competitively against these algorithms on the majority of 34 continuous benchmark problems, and it also shows obvious advantages in solving multiobjective 0-1 knapsack problems.

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