LCS & GBML Central

In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto-optimal solutions using a limited population size as the number of objectives increases. This paper proposes a reference vector-guided EA for many-objective optimization. The reference vectors can be used not only to decompose the original multiobjective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the reference vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art EAs for many-objective optimization. In addition, we show that reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization. Furthermore, a reference vector regeneration strategy is proposed for handling irregular PFs. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.

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

Evolutionary Computation, Ahead of Print.

Evolutionary Computation, Ahead of Print.

The deadline for submissions to the Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation has been extended for two weeks, to October 15, 2016.

The deadline for submissions to the Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation has been extended for two weeks, to October 15, 2016.

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

It contains:

“Learning to rank: new approach with the layered multi-population genetic programming on click-through features”
by Amir Hosein Keyhanipour, Behzad Moshiri, Farhad Oroumchian, Maseud Rahgozar & Kambiz Badie

“Prediction of the natural gas consumption in chemical processing facilities with genetic programming”
by Miha Kovačič & Franjo Dolenc

“Unveiling the properties of structured grammatical evolution”
by Nuno Lourenço, Francisco B. Pereira & Ernesto Costa

“An automatic solver for very large jigsaw puzzles using genetic algorithms”
by Dror Sholomon, Omid E. David & Nathan S. Netanyahu

BOOK REVIEW
“Mike Preuss: Multimodal optimization by means of evolutionary algorithms”
by Nailah Al-Madi

BOOK REVIEW
Wolfgang Banzhaf and Lidia Yamamoto: Artificial Chemistries, MIT Press, 2015
by Kyle I. S. Harrington

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

It contains:

“Learning to rank: new approach with the layered multi-population genetic programming on click-through features”
by Amir Hosein Keyhanipour, Behzad Moshiri, Farhad Oroumchian, Maseud Rahgozar & Kambiz Badie

“Prediction of the natural gas consumption in chemical processing facilities with genetic programming”
by Miha Kovačič & Franjo Dolenc

“Unveiling the properties of structured grammatical evolution”
by Nuno Lourenço, Francisco B. Pereira & Ernesto Costa

“An automatic solver for very large jigsaw puzzles using genetic algorithms”
by Dror Sholomon, Omid E. David & Nathan S. Netanyahu

BOOK REVIEW
“Mike Preuss: Multimodal optimization by means of evolutionary algorithms”
by Nailah Al-Madi

BOOK REVIEW
Wolfgang Banzhaf and Lidia Yamamoto: Artificial Chemistries, MIT Press, 2015
by Kyle I. S. Harrington

Evolutionary Computation, Volume 24, Issue 4, Page 667-694, Winter 2016.

Evolutionary Computation, Volume 24, Issue 4, Page 695-718, Winter 2016.