LCS & GBML Central

Presents a listing of the reviewers who contributed to this publication in 2016.

Decomposition-based algorithms have become increasingly popular for evolutionary multiobjective optimization. However, the effect of scalarizing methods used in these algorithms is still far from being well understood. This paper analyzes a family of frequently used scalarizing methods, the ${L} _{ {p}}$ methods, and shows that the ${p}$ value is crucial to balance the selective pressure toward the Pareto optimal and the algorithm robustness to Pareto optimal front (PF) geometries. It demonstrates that an ${L} _{ {p}}$ method that can maximize the search ability of a decomposition-based algorithm exists and guarantees that, given some weight, any solution along the PF can be found. Moreover, a simple yet effective method called Pareto adaptive scalarizing (PaS) approximation is proposed to approximate the optimal ${p}$ value. In order to demonstrate the effectiveness of PaS, we incorporate PaS into a state-of-the-art decomposition-based algorithm, i.e., multiobjective evolutionary algorithm based on decomposition (MOEA/D), and compare the resultant MOEA/D-PaS with some other MOEA/D variants on a set of problems with different PF geometries and up to seven conflicting objectives. Experimental results demonstrate that the PaS is effective.

Presents the table of contents for this issue of the publication.

Provides a listing of the editorial board, current staff, committee members and society officers.

Despite their proven effectiveness, many Michigan learning classifier systems (LCSs) cannot perform multistep reinforcement learning in continuous spaces. To meet this technical challenge, some LCSs have been designed to learn fuzzy logic rules. They can be largely classified into strength-based and accuracy-based systems. The latter is gaining more research attention in the last decade. However, existing accuracy-based learning systems either address primarily single-step learning problems or require the action space to be discrete. In this paper, a new accuracy-based learning fuzzy classifier system (LFCS) is developed to explicitly handle continuous state input and continuous action output during multistep reinforcement learning. Several technical improvements have been achieved while developing the new learning algorithm. Particularly, we have successfully extended ${Q}$ -learning like credit assignment methods to continuous spaces. To enable direct learning of stochastic strategies for action selection, we have also proposed to use a new fuzzy logic system with stochastic action outputs. Moreover, fine-grained learning of fuzzy rules has been achieved effectively in our algorithm by using a natural gradient learning method. It is the first time that these techniques are utilized substantially in any accuracy-based LFCSs. Meanwhile, in comparison with several recently proposed learning algorithms, our algorithm is shown to perform highly competitively on four benchmark learning problems and a robotics problem. The practical usefulness of our algorithm is also demonstrated by improving the performance of a wireless body area network.

Advertisement, IEEE.

Evolutionary Computation, Ahead of Print.

Evolutionary Computation, Ahead of Print.

The fourth issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

“A two-objective memetic approach for the node localization problem in wireless sensor networks” by Mahdi Aziz, Mohammad-H Tayarani-N & Mohammad R. Meybodi

“Evolution of sustained foraging in three-dimensional environments with physics” by Nicolas Chaumont & Christoph Adami

“Dynamic feedback neuro-evolutionary networks for forecasting the highly fluctuating electrical loads” by Gul Muhammad Khan & Faheem Zafari

“Prediction of expected performance for a genetic programming classifier” by Yuliana Martínez, Leonardo Trujillo, Pierrick Legrand & Edgar Galván-López

The fourth issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download. It contains: “A two-objective memetic approach for the node localization problem in wireless sensor networks” by Mahdi Aziz, Mohammad-H Tayarani-N & Mohammad R. Meybodi “Evolution of sustained foraging in three-dimensional environments with physics” by Nicolas Chaumont & Christoph Adami “Dynamic feedback neuro-evolutionary networks for forecasting the highly fluctuating electrical loads” by Gul Muhammad Khan & Faheem Zafari “Prediction of expected performance for a genetic programming classifier” by Yuliana Martínez, Leonardo Trujillo, Pierrick Legrand & Edgar Galván-López