Author: Community

Model management plays an essential role in surrogate-assisted evolutionary optimization of expensive problems, since the strategy for selecting individuals for fitness evaluation using the real objective function has substantial influences on the final performance. Among many others, infill criterion driven Gaussian process (GP)-assisted evolutionary algorithms have been demonstrated competitive for optimization of problems with up to 50 decision variables. In this paper, a multiobjective infill criterion (MIC) that considers the approximated fitness and the approximation uncertainty as two objectives is proposed for a GP-assisted social learning particle swarm optimization algorithm. The MIC uses nondominated sorting for model management, thereby avoiding combining the approximated fitness and the approximation uncertainty into a scalar function, which is shown to be particularly important for high-dimensional problems, where the estimated uncertainty becomes less reliable. Empirical studies on 50-D and 100-D benchmark problems and a synthetic problem constructed from four real-world optimization problems demonstrate that the proposed MIC is more effective than existing scalar infill criteria for GP-assisted optimization given a limited computational budget.

Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as linear constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a linear manifold defined by the constraints. The objective function is only evaluated at feasible search points (interior point method). This is a property often required in application domains, such as simulation optimization and finite element methods. The algorithm is tested on a variety of different test problems revealing considerable results.

The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed.

Geometric semantic genetic programming (GP) has recently attracted much attention. The key innovations are inducing a unimodal fitness landscape in the semantic space and providing a theoretical framework for designing geometric semantic operators. The geometric semantic operators aim to manipulate the semantics of programs by making a bounded semantic impact and generating child programs with similar or better behavior than their parents. These properties are shown to be highly related to a notable generalization improvement in GP. However, the potential ineffectiveness and difficulties in bounding the variations in these geometric operators still limits their positive effect on generalization. This paper attempts to further explore the geometry and search space of geometric operators to gain a greater generalization improvement in GP for symbolic regression. To this end, a new angle-driven selection operator and two new angle-driven geometric search operators are proposed. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, be resistant to overfitting. The experiments show that compared with two state-of-the-art geometric semantic operators, our angle-driven geometric operators not only drive the evolutionary process to fit the target semantics more efficiently but also improve the generalization performance. A further comparison between the evolved models shows that the new method generally produces simpler models with a much smaller size and is more likely to evolve toward the correct structure of the target models.

In this paper, a novel and fast memetic evolutionary algorithm is presented which can solve fully constrained generic inverse kinematics with multiple end effectors and goal objectives, leaving high flexibility for the design of custom cost functions. The algorithm utilizes a hybridization of evolutionary and swarm optimization, combined with the limited-memory-Broyden–Fletcher–Goldfarb–Shanno with bound constraints algorithm for gradient-based optimization. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided, scaling well even for greatly higher degree of freedom. The algorithm provides a general framework for bounded continuous optimization which only requires two parameters for the number of individuals and elites to be set, and supports adding additional goals and constraints for inverse kinematics, such as minimal displacement between solutions, collision avoidance, or functional joint relations. Experimental results on several industrial and anthropomorphic robots as well as on virtual characters demonstrate the algorithm to be applicable for solving complex kinematic postures for different challenging tasks in robotics, human-robot interaction and character animation, including dexterous object manipulation, collision-free full-body motion, as well as animation post-processing for video games and films. Implementations are made available for Unity3D and robot operating system.

This paper suggests a novel clustering-based evolutionary algorithm for many-objective optimization problems. Its main idea is to classify the population into a number of clusters, which is expected to solve the difficulty of balancing convergence and diversity in high-dimensional objective space. The individuals showing high similarities on the vector angles are gathered into the same cluster, such that the population’s distribution can be well portrayed by the clusters. To efficiently find these clusters, partitional clustering is first used to classify the union population into ${m}$ main clusters based on the ${m}$ axis vectors ( ${m}$ is the number of objectives), and then hierarchical clustering is further run on these ${m}$ main clusters to get ${N}$ final clusters ( ${N}$ is the population size and ${N>m}$ ). At last, in environmental selection, one individual from each of ${N}$ clusters closest to the axis vectors is selected to maintain diversity, while one individual from each of the other clusters is preferred by a simple convergence indicator to ensure convergence. When tackling some well-known test problems with 5–15 objectives, extensive experiments validate the superiority of our algorithm over six competitive many-objective EAs, especially on problems with incomplete and irregular Pareto-optimal fronts.