Author: Community

In offline data-driven optimization, only historical data is available for optimization, making it impossible to validate the obtained solutions during the optimization. To address these difficulties, this paper proposes an evolutionary algorithm assisted by two surrogates, one coarse model and one fine model. The coarse surrogate (CS) aims to guide the algorithm to quickly find a promising subregion in the search space, whereas the fine one focuses on leveraging good solutions according to the knowledge transferred from the CS. Since the obtained Pareto optimal solutions have not been validated using the real fitness function, a technique for generating the final optimal solutions is suggested. All achieved solutions during the whole optimization process are grouped into a number of clusters according to a set of reference vectors. Then, the solutions in each cluster are averaged and outputted as the final solution of that cluster. The proposed algorithm is compared with its three variants and two state-of-the-art offline data-driven multiobjective algorithms on eight benchmark problems to demonstrate its effectiveness. Finally, the proposed algorithm is successfully applied to an operational indices optimization problem in beneficiation processes.

Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This article presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency. In the proposed algorithm, two phases, namely, the global exploration and the local exploitation, are performed in each generation. In the global phase, a set of trial vectors is produced for each target individual by employing multiple strategies with strong exploration capability. Afterward, an adaptive underestimation model with an self-adapted slope control parameter is proposed to evaluate these trial vectors, the best of which is selected as the candidate. In the local phase, the better-based strategies guided by individuals that are better than the target individual are designed. For each individual accepted in the global phase, multiple trial vectors are generated by using these strategies and filtered by the underestimation value. The cooperation between the global and local phases includes two aspects. First, both of them concentrate on generating better individuals for the next generation. Second, the global phase aims to locate promising regions quickly while the local phase serves as a local search for enhancing convergence. Moreover, a simple mechanism is designed to determine the parameter of DE adaptively in the searching process. Finally, the proposed approach is applied to predict the protein 3-D structure. The experimental studies on classical benchmark functions, CEC test sets, and protein structure prediction problem show that the proposed approach is superior to the competitors.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

A theoretical performance analysis of the $(mu /mu _{I},lambda) – sigma $ -self-adaptation evolution strategy ( $sigma $ SA-ES) is presented considering a conically constrained problem. Infeasible offspring are repaired using projection onto the boundary of the feasibility region. Closed-form approximations are used for the one-generation progress of the evolution strategy. Approximate deterministic evolution equations are formulated for analyzing the strategy’s dynamics. By iterating the evolution equations with the approximate one-generation expressions, the evolution strategy’s dynamics can be predicted. The derived theoretical results are compared to experiments for assessing the approximation quality. It is shown that in the steady state the $(mu /mu _{I},lambda) – sigma $ SA-ES exhibits a performance as if the ES were optimizing a sphere model. Unlike the nonrecombinative $(1,lambda)$ -ES, the parental steady state behavior does not evolve on the cone boundary but stays away from the boundary to a certain extent.

The first/second issue of Volume 21 of Genetic Programming and Evolvable Machines, a Twentieth Anniversary Special Issue double issue, is now available for download.

It contains:

Editorial introduction
Lee Spector

GP+EM 20th anniversary editorial
Nicholas Freitag McPhee, William B. Langdon

Genetic programming for natural language processing
Lourdes Araujo

Applications of genetic programming to finance and economics: past, present, future
Anthony Brabazon, Michael Kampouridis

Evolutionary music: applying evolutionary computation to the art of creating music
Róisín Loughran, Michael O’Neill

The impact of genetic programming in education
Nelishia Pillay

Genetic programming in the steelmaking industry
Miha Kovačič, Uroš Župerl

Cartesian genetic programming: its status and future
Julian Francis Miller

Genetic programming theory and practice: a fifteen-year trajectory
Moshe Sipper, Jason H. Moore

Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence
Andrea De Lorenzo, Alberto Bartoli

Genetic programming and evolvable machines at 20
W. B. Langdon

Adversarial genetic programming for cyber security: a rising application domain where GP matters
Una-May O’Reilly, Jamal Toutouh

Automatic programming: The open issue?
Michael O’Neill, Lee Spector

BOOK REVIEW
Juan C. Burguillo: Self-organizing coalitions for managing complexity
B. Ombuki-Berman

BOOK REVIEW
Joseph E. Aoun: Robot-proof: higher education at the age of artificial intelligence
Rosa Leonor Ulloa-Cazarez

SOFTWARE REVIEW
Inspyred: Bio-inspired algorithms in Python
Alberto Tonda

SOFTWARE REVIEW
Software review: the GPTIPS platform

Acknowledgment to reviewers (2019)
Lee Spector

Evolutionary Computation, Volume 29, Issue 1, Page 75-105, Spring 2021.

Evolutionary Computation, Volume 28, Issue 4, Page 677-708, Winter 2020.

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

Dynamic multiobjective optimization problems (DMOPs) challenge multiobjective evolutionary algorithms (MOEAs) because those problems change rapidly over time. The class of DMOPs whose objective functions change over time steps, in ways that exhibit some hidden patterns has gained much attention. Their predictability indicates that the problem exhibits some correlations between solutions obtained in sequential time periods. Most of the current approaches use linear models or similar strategies to describe the correlations between historical solutions obtained, and predict the new solutions in the following time period as an initial population from which the MOEA can begin searching in order to improve its efficiency. However, nonlinear correlations between historical solutions and current solutions are more common in practice, and a linear model may not be suitable for the nonlinear case. In this paper, we present a support vector regression (SVR)-based predictor to generate the initial population for the MOEA in the new environment. The basic idea of this predictor is to map the historical solutions into a high-dimensional feature space via a nonlinear mapping, and to do linear regression in this space. SVR is used to implement this process. We incorporate this predictor into the MOEA based on decomposition (MOEA/D) to construct a novel algorithm for solving the aforementioned class of DMOPs. Comprehensive experiments have shown the effectiveness and competitiveness of our proposed predictor, comparing with the state-of-the-art methods.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.