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With the continuous advancement of industry 4.0, also in the area of production and logistics optimization, a more holistic consideration of problems is required. Therefore, in contrary to the traditional sequential optimization approach in the area of ...
In this work, we evaluate an asynchronous population-based algorithm following a pool-based approach. In Pool-based algorithms, a collection of workers collaborates through a shared population repository. In particular, we followed the EvoSpace approach ...
Although the use of ensemble methods in machine-learning is ubiquitous, ensembles of optimisation algorithms have received relatively little attention. In [2] we address fundamental questions regarding ensemble composition in optimisation using the ...
Coupled models of the soil-vegetation-atmosphere systems are increasingly used to investigate interactions between the system components. Due to the different spatial and temporal scales of relevant processes and computational restrictions, the ...
Multi-objective optimization problems with changing variables are very common in real-world applications. This kind of problems often has a changing Pareto-optimal set and a complex relation among decision variables. In order to rapidly track the time-...
In this paper an improved version of a general-purpose asynchronous adaptive multi-population model for distributed Differential Evolution algorithm is investigated. Specifically, in addition to an asynchronous mechanism for a multi-population ...
This paper proposes Extreme Learning Surrogate assisted Asynchronous Multi-Objective Optimization Based on Decomposition (AELMOEA/D) that solves multi-objective optimization problems with expensive and different evaluation time by integrating a ...
In recent years, research on large scale global optimization (LSGO) provided metaheuristics able to effectively tackle real-valued objective functions depending on thousand of variables. Nevertheless, finding a suitable solution of LSGO problems often ...
The simultaneous optimization of multiple objectives arises in several problems in different disciplines. This optimization, mainly for many-objective problems brings challenges to the state-of-the-art Multi-Objective Evolutionary Algorithms. Given the ...
The aim of the paper is to introduce a new parallel approach to evolutionary optimization of digital circuits described on transistor level. The evolutionary optimization is guided by the fitness function employing a simulator of candidate circuits. A ...
The need of manual hyper-parameter selection can seriously hamper the model optimization of Deep Neural Networks (DNNs). Conventional automated approaches tackling this problem suffer from poor scalability or fail in certain scenarios. In this paper, we ...
In this paper, we investigate a new approach for adapting population size in the CMA-ES. This method is based on tracking the information in each slot of S successive iterations to decide whether we should increase or decrease or keep the population ...
In this paper, Gaussian processes are studied in connection with the state-of-the-art method for continuous black-box optimization CMA-ES. To combine them with the CMA-ES is challenging because CMA-ES invariance with respect to order preserving ...
Variation operators have seemingly been less in the focus than selection operators during the first years of research on evolutionary multiobjective optimization. Several new developments in benchmarking and hypervolume selection have now sparked a ...
Self-Adaptive Search Equation based Artificial Bee Colony (SSEABC) is a recent variant of Artificial Bee Colony (ABC) algorithm. SSEABC proposed three enhancements on the canonical ABC algorithm. These are the self-adaptive search equation selection ...
Miniature autonomous sensory agents (MASA) can play a profound role in the exploration of hardly accessible unknown environments, thus, impacting many applications such as monitoring of underground infrastructure or exploration for natural resources, ...
We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo evolution in an on-line fashion. In these studies, we have been using various types of robot morphologies and ...
The relation between diversity and genotype to phenotype mapping has been the focus of several studies. In those Evolutionary Algorithms (EAs) where the genotype is a sequence of symbols, the contribution of each of those symbols in determining the ...
We, as well as others, have already shown in previous works that reproductive isolation and a large population size are critical to achieve behavioral specialization in embodied evolutionary robotics. Here, we extend our previous work from [3] by ...
Recent research in small populations of Thymio II robots illustrated the relative benefits of populations distinguishing heritable and learning features in robots for a simple obstacle avoidance task. Here we scientifically validate these results by ...