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Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection [chapter]

Xavier Llorà, David E. Goldberg, Ivan Traus, Ester Bernadó
2003 Lecture Notes in Computer Science  
the Pareto front.  ...  Using the proposed multiobjective approach a set of compromise hypotheses are spread along the Pareto front.  ...  Acknowledgments This work was sponsored by the Air Force Office of Scientific Research, Air Force Materiel Command, USAF, under grant F49620-00-0163, and by the Technology Research Center (TRECC), a program  ... 
doi:10.1007/978-3-540-40029-5_8 fatcat:pqkt52kd2feonopmgihbe4u5ge

Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)

Carola Doerr, Christian Igel, Lothar Thiele, Xin Yao, Marc Herbstritt
2017 Dagstuhl Reports  
Schloss Dagstuhl -Leibniz Center for Informatics.  ...  This report summarizes the talks, breakout sessions, and discussions at the Dagstuhl Seminar 17191 on Theory of Randomized Optimization Heuristics, held during the week from May 08 until May 12, 2017, in  ...  indicator for assessing the quality of Pareto front approximations if equispaced solutions on the Pareto front are desired.This may happen frequently in dynamic control applications.Recently there have  ... 
doi:10.4230/dagrep.7.5.22 dblp:journals/dagstuhl-reports/DoerrIT017 fatcat:guma4eanyne6vlkwexkevs4v6i

Hybrid Multiobjective Evolutionary Algorithms in Small Molecule De Novo Design

C.A. Nicolaou, C.S. Pattichis, C.S. Schizas, C.I. Christodoulou, D. Fotiades, G. Kontaxakis
2010 Zenodo  
In comparisons with commonly used algorithms, MEGA is found to produce statistically significant better results. GRAPH DESIGN USING KNOWLEDGE-DRIVEN,  ...  In the experimental section we present results for the problem of designing molecules satisfying multiple pharmaceutically relevant objectives.  ...  Genetic Programming Genetic Programming (GP) is an extension of the GA that uses a procedural or functional representation for solutions [PLP08] .  ... 
doi:10.5281/zenodo.2592431 fatcat:eolxugjcyzcspnxy2owmrjm7ei

Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning [chapter]

Faustino J. Gomez, Julian Togelius, Juergen Schmidhuber
2009 Lecture Notes in Computer Science  
For sequential decision tasks, phenotypes that are very similar in structure, can produce radically different behaviors, and the trade-off between fitness and complexity in this context is not clear.  ...  However, EC research has only focused phenotype structural complexity for static problems.  ...  Acknowledgments This research was supported in part by the EU Projects IM-CLEVER (#231711), STIFF (#231576), Humanobs (#231453), and the NSF under grant EIA-0303609.  ... 
doi:10.1007/978-3-642-04277-5_77 fatcat:ar4x4bdbozekthcjwjj4dm7j6q

Multi-agent based hyper-heuristics for multi-objective flexible job shop scheduling: A case study in an aero-engine blade manufacturing plant

Yong Zhou, Jian-Jun Yang, Lian-Yu Zheng
2019 IEEE Access  
SPEA2 Strength Pareto evolutionary algorithm 2. 2/3/MPGP Multi-objective cooperative coevolution genetic programming with two/three/multiple sub-populations.  ...  INDEX TERMS Scheduling, flexible job shop, multi-agent, hyper-heuristics, genetic programming. NOMENCLATURE NSGAII Nondominated sorting genetic algorithm II.  ...  ACKNOWLEDGEMENT The authors would like to thank Qing-miao Liao and Peng-cheng Fang for their support and contributions during the development of this work.  ... 
doi:10.1109/access.2019.2897603 fatcat:zpysik5q6ranrbele6r7zz2pqa

Automated Discovery of Relationships, Models, and Principles in Ecology

Pedro Cardoso, Vasco V. Branco, Paulo A. V. Borges, José C. Carvalho, François Rigal, Rosalina Gabriel, Stefano Mammola, José Cascalho, Luís Correia
2020 Frontiers in Ecology and Evolution  
Third, we illustrate how SR can be used to find general models in ecology, namely new formulas for species richness estimators and the general dynamic model of oceanic island biogeography.  ...  Here we explored the potential of symbolic regression (SR), commonly used in other areas, in the field of ecology.  ...  ACKNOWLEDGMENTS We thanked Robert Whittaker, Stano Pekár, Michael Lavine, and Otso Ovaskainen for comments on earlier versions of the manuscript; Carla Gomes and Ronan Le Bras for fruitful discussions  ... 
doi:10.3389/fevo.2020.530135 fatcat:lntqdipodfdo5bijqhhz7cktpy

Automated Learning of Interpretable Models with Quantified Uncertainty [article]

G.F. Bomarito and P.E. Leser and N.C.M Strauss and K.M. Garbrecht and J.D. Hochhalter
2022 arXiv   pre-print
A new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is introduced that uses model evidence (i.e., marginal likelihood) to formulate replacement probability during the selection  ...  Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior.  ...  In the current work, a new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is developed.  ... 
arXiv:2205.01626v1 fatcat:ggxhsptf4zc23ayvvvgomladwi

Creating a Digital Ecosystem: Service-Oriented Architectures with Distributed Evolutionary Computing [article]

G Briscoe
2012 arXiv   pre-print
We start with a discussion of the relevant literature, including Nature Inspired Computing as a framework in which to understand this work, and the process of biomimicry to be used in mimicking the necessary  ...  This leads to a discussion of the relevant fields from computer science for the creation of Digital Ecosystems, including evolutionary computing, Multi-Agent Systems, and Service-Oriented Architectures  ...  Acknowledgments The author would like to thank for their encouragement and suggestions: my supervisor  ... 
arXiv:0712.4159v5 fatcat:cgfm5yvlezcbpeal6qxczroh64

Evolving Distributed Algorithms With Genetic Programming

Thomas Weise, Ke Tang
2012 IEEE Transactions on Evolutionary Computation  
mechanisms is prefered by Genetic Programming.  ...  In Genetic Programming, bloat is the uncontrolled growth in size of the individuals during the course of the evolution [165].  ... 
doi:10.1109/tevc.2011.2112666 fatcat:6ugekhdccvaxlktpe2tphuaxeu

Evolving distributed algorithms with genetic programming

Thomas Weise, Michael Zapf
2009 Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC '09  
mechanisms is prefered by Genetic Programming.  ...  In Genetic Programming, bloat is the uncontrolled growth in size of the individuals during the course of the evolution [165].  ... 
doi:10.1145/1543834.1543913 dblp:conf/gecco/WeiseZ09 fatcat:zssxruduxbcrnkvtkvbkk57mku

Digital Ecosystems: Ecosystem-Oriented Architectures

Gerard Briscoe, Suzanne Sadedin, Philippe De Wilde
2011 Natural Computing  
This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity).  ...  Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications.  ...  it is duplicated in the sequence; analogously to genetic programming [7] , we can call these redundant Agents bloat.  ... 
doi:10.1007/s11047-011-9254-0 fatcat:7uptgmhphbbo7fvcrbnzwi57kq

Modelling Evolvability in Genetic Programming [chapter]

Benjamin Fowler, Wolfgang Banzhaf
2016 Lecture Notes in Computer Science  
In genetic programming (GP), we typically measure how well a program performs a given task at its current capacity only. We improve upon GP by directly selecting for evolvability.  ...  MEGP is empirically shown to improve generational fitness for a streaming domain, in exchange for an upfront increase in computational time. ii  ...  AGP differs from standard genetic programming (SGP) by using adaptive control patterns and adaptive elitism.  ... 
doi:10.1007/978-3-319-30668-1_14 fatcat:m46sps5aonfj7e5hswsaekprc4

Automated discovery of relationships, models and principles in ecology [article]

Pedro Cardoso, Paulo AV Borges, Jose C Carvalho, Francois Rigal, Rosalina Gabriel, Jose Cascalho, Luis Correia
2015 bioRxiv   pre-print
First, we demonstrate how SR can deal with complex datasets for: 1) modelling species richness; and 2) modelling species spatial distributions.  ...  Finding hidden relationships in complex data is now possible through the use of massive computational power, particularly by means of Artificial Intelligence methods, such as evolutionary computation.  ...  However, in all cases it is important to 146 check all formulas along the Pareto front.  ... 
doi:10.1101/027839 fatcat:dtbyfz3rcffnlbqq5cvwnjvwhu

Comparison of semantic-based local search methods for multiobjective genetic programming

Tiantian Dou, Peter Rockett
2018 Genetic Programming and Evolvable Machines  
We report a series of experiments that use semantic-based local search within a multiobjective genetic programming (GP) framework.  ...  We compare various ways of selecting target subtrees for local search as well as different methods for performing that search; we have also made comparison with the random desired operator of Pawlak et  ...  distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in  ... 
doi:10.1007/s10710-018-9325-4 fatcat:5ztu2iab3vcs3gqc3rpbnzgxxe

Search-based software engineering

Mark Harman, S. Afshin Mansouri, Yuanyuan Zhang
2012 ACM Computing Surveys  
The interest in SBO for SE has led to an increased interest in other forms of optimization for SE that are not necessarily directly based on a 'search'.  ...  This is because fitness is computed directly in terms of the engineering artifact, without the need for the simulation and modelling inherent in all other approaches to engineering optimization.  ...  In the figure, points S1, S2 and S3 lie on the Pareto front, while S4 and S5 are dominated.  ... 
doi:10.1145/2379776.2379787 fatcat:m4elb2acwvfdjaifqdcjftgksq
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