Author Archives: Community

Sharing GPEM articles

This isn’t news, but I don’t think it is yet as widely known as it should be: GPEM authors can post shareable links to view-only versions of their articles, through Springer’s “SharedIt” service. The details are here. Continue reading

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GPEM 18(4) is available

The fourth issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download. It contains:

A meta-grammatical evolutionary process for portfolio selection and trading
by Iván Contreras, J. Ignacio Hidalgo, Laura Nuñez-Letamendía & J. Manuel Velasco

Affective evolutionary music composition with MetaCompose
by Marco Scirea, Julian Togelius, Peter Eklund & Sebastian Risi

Understanding grammatical evolution: initialisation
by Miguel Nicolau

BOOK REVIEW
Gustavo Olague: Evolutionary computer vision, the first footprints
by Evelyne Lutton

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IEEE Transactions on Evolutionary Computation information for authors

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. Continue reading

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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$_{mathbf{CTC}}$ . Continue reading

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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 genera… Continue reading

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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. Continue reading

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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. Continue reading

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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 oppositi… Continue reading

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