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Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction
Advanced Robotics Research, EarlyView.Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith +4 more
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From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures
Advanced Robotics Research, EarlyView.This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley +1 more source
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Information Sciences, 2001
This article broadly introduces evolutionary algorithms and discusses the current trends, both in a historical perspective and with respect to practical outcomes. It then quickly surveys theoretical results and main domains of applications.
Michalewicz, Z., Schoenauer, M.
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This article broadly introduces evolutionary algorithms and discusses the current trends, both in a historical perspective and with respect to practical outcomes. It then quickly surveys theoretical results and main domains of applications.
Michalewicz, Z., Schoenauer, M.
openaire +3 more sources
Journal of Computational Science, 2021
Abstract This paper proposes an evolutionary Rao algorithm (ERA) to enhance three state-of-the-art metaheuristic Rao algorithms (Rao-1, Rao-2, Rao-3) by introducing two new schemes. Firstly, the population is split into two sub-populations based on their qualities: high and low, with a particular portion. The high-quality sub-population searches for
Suyanto Suyanto +4 more
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Abstract This paper proposes an evolutionary Rao algorithm (ERA) to enhance three state-of-the-art metaheuristic Rao algorithms (Rao-1, Rao-2, Rao-3) by introducing two new schemes. Firstly, the population is split into two sub-populations based on their qualities: high and low, with a particular portion. The high-quality sub-population searches for
Suyanto Suyanto +4 more
openaire +2 more sources
WIREs Data Mining and Knowledge Discovery, 2014
AbstractEvolutionary algorithm (EA) is an umbrella term used to describe population‐based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming.
Thomas Bartz-Beielstein +3 more
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AbstractEvolutionary algorithm (EA) is an umbrella term used to describe population‐based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming.
Thomas Bartz-Beielstein +3 more
openaire +2 more sources
Evolving evolutionary algorithms using evolutionary algorithms
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem.
Laura Diosan, Mihai Oltean
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Evolutionary design of Evolutionary Algorithms
Genetic Programming and Evolvable Machines, 2009Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters
Laura Diosan, Mihai Oltean
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Quasirandom evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010Motivated by recent successful applications of the concept of quasirandomness, we investigate to what extent such ideas can be used in evolutionary computation. To this aim, we propose different variations of the classical (1+1) evolutionary algorithm, all imitating the property that the (1+1) EA over intervals of time touches all bits roughly the same
Benjamin Doerr +2 more
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Suitability of evolutionary algorithms for evolutionary testing
Proceedings 26th Annual International Computer Software and Applications, 2003Evolutionary testing is based on the principle of searching for relevant test cases in the input domain of the system under test with the help of evolutionary algorithms. Evolutionary testing enables the complete automation of test case design whenever the test aim can be expressed numerically, e.g.
Joachim Wegener +2 more
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The immune evolutionary algorithm
1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410), 2003A novel algorithm, the immune evolutionary algorithm (IEA), is proposed based on immune theory in biology, which constructs an immune operator accomplished by two steps: a vaccination and an immune selection. IEA is shown to converge to the global optimum with probability 1.
Lei Wang 0018, Licheng Jiao
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