LCS & GBML Central

The second issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

An iterative genetic programming approach to prototype generation
by José María Valencia-Ramírez, Mario Graff, Hugo Jair Escalante & Jaime Cerda-Jacobo

Recursion in tree-based genetic programming
by Alexandros Agapitos, Michael O’Neill, Ahmed Kattan & Simon M. Lucas

Recurrent Cartesian Genetic Programming of Artificial Neural Networks
by Andrew James Turner & Julian Francis Miller

An analysis of the genetic marker diversity algorithm for genetic programming
by Armand R. Burks & William F. Punch

Solving metameric variable-length optimization problems using genetic algorithms
by Matthew L. Ryerkerk, Ronald C. Averill, Kalyanmoy Deb & Erik D. Goodman

SOFTWARE REVIEW
Software review: CGP-Library
by Emerson Carlos Pedrino, Paulo Cesar Donizeti Paris, Denis Pereira de Lima & Valentin Obac Roda

The second issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

An iterative genetic programming approach to prototype generation
by José María Valencia-Ramírez, Mario Graff, Hugo Jair Escalante & Jaime Cerda-Jacobo

Recursion in tree-based genetic programming
by Alexandros Agapitos, Michael O’Neill, Ahmed Kattan & Simon M. Lucas

Recurrent Cartesian Genetic Programming of Artificial Neural Networks
by Andrew James Turner & Julian Francis Miller

An analysis of the genetic marker diversity algorithm for genetic programming
by Armand R. Burks & William F. Punch

Solving metameric variable-length optimization problems using genetic algorithms
by Matthew L. Ryerkerk, Ronald C. Averill, Kalyanmoy Deb & Erik D. Goodman

SOFTWARE REVIEW
Software review: CGP-Library
by Emerson Carlos Pedrino, Paulo Cesar Donizeti Paris, Denis Pereira de Lima & Valentin Obac Roda

Evolutionary Computation, Volume 26, Issue 2, Page 269-297, Summer 2018.

Evolutionary Computation, Volume 26, Issue 3, Page 471-505, Fall 2018.

Evolutionary Computation, Volume 26, Issue 3, Page 471-505, Fall 2018.

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

Interactions between decision variables typically make an optimization problem challenging for an evolutionary algorithm (EA) to solve. Exploratory landscape analysis (ELA) techniques can be used to quantify the level of variable interactions in an optimization problem. However, many studies using ELA techniques to investigate interactions have been limited to combinatorial problems, with very few studies focused on continuous variables. In this paper, we propose a novel ELA measure to quantify the level of variable interactions in continuous optimization problems. We evaluated the efficacy of this measure using a suite of benchmark problems, consisting of 24 multidimensional continuous optimization functions with differing levels of variable interactions. Significantly, the results reveal that our measure is robust and can accurately identify variable interactions. We show that the solution quality found by an EA is correlated with the level of variable interaction in a given problem. Finally, we present the results from simulation experiments illustrating that when our measure is embedded into an algorithm design framework, the enhanced algorithm achieves equal or better results on the benchmark functions.

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.

Sparse unmixing of hyperspectral data is an important technique aiming at estimating the fractional abundances of the end members. Traditional sparse unmixing is faced with the l₀-norm problem which is an NP-hard problem. Sparse unmixing is inherently a multiobjective optimization problem. Most of the recent works combine cost functions into single one to construct an aggregate objective function, which involves weighted parameters that are sensitive to different data sets and difficult to tune. In this paper, a novel multiobjective cooperative coevolutionary algorithm is proposed to optimize the reconstruction term, the sparsity term and the total variation regularization term simultaneously. A problem-dependent cooperative coevolutionary strategy is designed because sparse unmixing encounters a large scale optimization problem. The proposed approach optimizes the nonconvex l0-norm problem directly and can find a better compromise between two or more competing cost function terms automatically. Experimental results on simulated and real hyperspectral data sets demonstrate the effectiveness of the proposed method.

Adaptive operator selection (AOS) is a high-level controller for an optimization algorithm that monitors the performance of a set of operators with a credit assignment strategy and adaptively applies the high performing operators with an operator selection strategy. AOS can improve the overall performance of an optimization algorithm across a wide range of problems, and it has shown promise on single-objective problems where defining an appropriate credit assignment that assesses an operator’s impact is relatively straightforward. However, there is currently a lack of AOS for multiobjective problems (MOPs) because defining an appropriate credit assignment is nontrivial for MOPs. To identify and examine the main factors in effective credit assignment strategies, this paper proposes a classification that groups credit assignment strategies by the sets of solutions used to assess an operator’s impact and by the fitness function used to compare those sets of solutions. Nine credit assignment strategies, which include five newly proposed ones, are compared experimentally on standard benchmarking problems. Results show that eight of the nine credit assignment strategies are effective in elevating the generality of a multiobjective evolutionary algorithm and outperforming a random operator selector.