GPEM 16(2) is now available. It contains:
“Evolving robot sub-behaviour modules using Gene Expression Programming”
by Jonathan Mwaura & Ed Keedwell
“Balanced Cartesian Genetic Programming via migration and opposition-based learning: application to symbolic regression”
by Samaneh Yazdani & Jamshid Shanbehzadeh
“A hierarchical genetic algorithm approach for curve fitting with B-splines”
by C. H. Garcia-Capulin, F. J. Cuevas, G. Trejo-Caballero & H. Rostro-Gonzalez
“Multiobjective optimization algorithms for motif discovery in DNA sequences”
by David L. González-Álvarez, Miguel A. Vega-Rodríguez & Álvaro Rubio-Largo
“Exploring non-photorealistic rendering with genetic programming”
by Maryam Baniasadi & Brian J. Ross
Presents information for authors publishing in this journal.
In evolutionary multiobjective optimization, it is very important to be able to visualize approximations of the Pareto front (called approximation sets) that are found by multiobjective evolutionary algorithms. While scatter plots can be used for visualizing 2-D and 3-D approximation sets, more advanced approaches are needed to handle four or more objectives. This paper presents a comprehensive review of the existing visualization methods used in evolutionary multiobjective optimization, showing their outcomes on two novel 4-D benchmark approximation sets. In addition, a visualization method that uses prosection (projection of a section) to visualize 4-D approximation sets is proposed. The method reproduces the shape, range, and distribution of vectors in the observed approximation sets well and can handle multiple large approximation sets while being robust and computationally inexpensive. Even more importantly, for some vectors, the visualization with prosections preserves the Pareto dominance relation and relative closeness to reference points. The method is analyzed theoretically and demonstrated on several approximation sets.
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
This paper is concerned with multiobjective evolutionary optimization under uncertainty modeled through probability distributions, with a focus on reliability-based approaches. The contribution is twofold. First, an in-depth study of the notion of probability of dominance is performed, including state-of-the-art multiobjective reliability-based formulations and their numerical calculation. In particular, the notion of dominance limit state function is defined and its properties are thoroughly investigated. Second, the assessment of the probability of dominance is proposed based on a first-order reliability method tailored for Pareto dominance and incorporated into a multiobjective evolutionary algorithm through a repairing mechanism. The analysis of the numerical results on five biobjective benchmark test cases (from two up to five design variables) by means of two adapted metrics (averaged Hausdorff distance and maximum Pareto front error) demonstrates the potential of the proposed approach to reach reliable nondominated fronts within a limited number of generations.
The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world’s energy supply mix, but remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. Initially, a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.
Evolutionary algorithms have been shown to be powerful for solving multiobjective optimization problems, in which nondominated sorting is a widely adopted technique in selection. This technique, however, can be computationally expensive, especially when the number of individuals in the population becomes large. This is mainly because in most existing nondominated sorting algorithms, a solution needs to be compared with all other solutions before it can be assigned to a front. In this paper we propose a novel, computationally efficient approach to nondominated sorting, termed efficient nondominated sort (ENS). In ENS, a solution to be assigned to a front needs to be compared only with those that have already been assigned to a front, thereby avoiding many unnecessary dominance comparisons. Based on this new approach, two nondominated sorting algorithms have been suggested. Both theoretical analysis and empirical results show that the ENS-based sorting algorithms are computationally more efficient than the state-of-the-art nondominated sorting methods.
In practical situations, it is very often desirable to detect multiple optimally sustainable solutions of an optimization problem. The population-based evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations to aid the parallel localized convergence of population members around different basins of attraction. This paper presents an improved information-sharing mechanism among the individuals of an evolutionary algorithm for inducing efficient niching behavior. The mechanism can be integrated with stochastic real-parameter optimizers relying on differential perturbation of the individuals (candidate solutions) based on the population distribution. Various real-coded genetic algorithms (GAs), particle swarm optimization (PSO), and differential evolution (DE) fit the example of such algorithms. The main problem arising from differential perturbation is the unequal attraction toward the different basins of attraction that is detrimental to the objective of parallel convergence to multiple basins of attraction. We present our study through DE algorithm owing to its highly random nature of mutation and show how population diversity is preserved by modifying the basic perturbation (mutation) scheme through the use of random individuals selected probabilistically. By integrating the proposed technique with DE framework, we present three improved versions of well-known DE-based niching methods. Through an extensive experimental analysis, a statistically significant improvement in the overall performance has been observed upon integrating of our technique with the DE-based niching methods.