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exploration exploitation tradeoff

Thomas Gueudré
We generalize a model of growth over a disordered environment, to a large class of Itō processes. In particular, we study how the microscopic properties of the noise influence the macroscopic growth rate. The present model can account for growth processes in large dimensions and provides a bed to understand better the tradeoff between exploration and exploitation. An additional mapping to the Schrödinger equation readily provides a set of disorders for which this model can be solved exactly. This mean-field approach exhibits interesting features, such as a freezing transition and an optimal point of growth, which can be studied in detail, and gives yet another explanation for the occurrence of the Zipf law in complex, well-connected systems...
April 2017: Physical Review. E
Man Ding, Hanning Chen, Na Lin, Shikai Jing, Fang Liu, Xiaodan Liang, Wei Liu
This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions...
March 2017: Saudi Journal of Biological Sciences
Weixing Su, Hanning Chen, Fang Liu, Na Lin, Shikai Jing, Xiaodan Liang, Wei Liu
There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff...
March 2017: Saudi Journal of Biological Sciences
Timothy J Fullman, Kyle Joly, Andrew Ackerman
BACKGROUND: Ungulate movements are influenced by a variety of biotic and abiotic factors, which may affect connectivity between key resource areas and seasonal ranges. In northwestern Alaska, one important question regarding human impacts on ungulate movement involves caribou (Rangifer tarandus) response to autumn hunting and related aircraft activity. While concerns have been voiced by local hunters about the influence of transporter aircraft and non-local sport hunters, there has been little quantitative analysis of the effects of hunter activity on caribou movement...
2017: Movement Ecology
M Chupeau, O Bénichou, S Redner
How to best exploit patchy resources? We introduce a minimal exploitation-migration model that incorporates the coupling between a searcher's trajectory, modeled by a random walk, and ensuing depletion of the environment by the searcher's consumption of resources. The searcher also migrates to a new patch when it takes S consecutive steps without finding resources. We compute the distribution of consumed resources F_{t} at time t for this non-Markovian searcher and show that consumption is maximized by exploring multiple patches...
January 2017: Physical Review. E
Shashi Thutupalli, Sravanti Uppaluri, George W A Constable, Simon A Levin, Howard A Stone, Corina E Tarnita, Clifford P Brangwynne
The ecological and evolutionary dynamics of populations are shaped by the strategies they use to produce and use resources. However, our understanding of the interplay between the genetic, behavioral, and environmental factors driving these strategies is limited. Here, we report on a Caenorhabditis elegans - Escherichia coli (worm-bacteria) experimental system in which the worm-foraging behavior leads to a redistribution of the bacterial food source, resulting in a growth advantage for both organisms, similar to that achieved via farming...
February 28, 2017: Proceedings of the National Academy of Sciences of the United States of America
Aizhu Zhang, Genyun Sun, Jinchang Ren, Xiaodong Li, Zhenjie Wang, Xiuping Jia
Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence...
January 2018: IEEE Transactions on Cybernetics
Peter Wittek, Ying-Hsang Liu, Sándor Darányi, Tom Gedeon, Ik Soo Lim
Information foraging connects optimal foraging theory in ecology with how humans search for information. The theory suggests that, following an information scent, the information seeker must optimize the tradeoff between exploration by repeated steps in the search space vs. exploitation, using the resources encountered. We conjecture that this tradeoff characterizes how a user deals with uncertainty and its two aspects, risk and ambiguity in economic theory. Risk is related to the perceived quality of the actually visited patch of information, and can be reduced by exploiting and understanding the patch to a better extent...
2016: Frontiers in Psychology
Frazer Meacham, Carl T Bergstrom
Normal anxiety is considered an adaptive response to the possible presence of danger, but is susceptible to dysregulation. Anxiety disorders are prevalent at high frequency in contemporary human societies, yet impose substantial disability upon their sufferers. This raises a puzzle: why has evolution left us vulnerable to anxiety disorders? We develop a signal detection model in which individuals must learn how to calibrate their anxiety responses: they need to learn which cues indicate danger in the environment...
2016: Evolution, Medicine, and Public Health
Hossam M Zawbaa, E Emary, Crina Grosan
BACKGROUND: Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used...
2016: PloS One
Timothé Collet, Olivier Pietquin
Active learning is the problem of interactively constructing the training set used in classification in order to reduce its size. It would ideally successively add the instance-label pair that decreases the classification error most. However, the effect of the addition of a pair is not known in advance. It can still be estimated with the pairs already in the training set. The online minimization of the classification error involves a tradeoff between exploration and exploitation. This is a common problem in machine learning for which multiarmed bandit, using the approach of Optimism in the Face of Uncertainty, has proven very efficient these last years...
2015: Computational Intelligence and Neuroscience
Stéphane Ross, Joelle Pineau
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation tradeoff in classical reinforcement learning. Unfortunately, the applicability of this type of approach has been limited to small domains due to the high complexity of reasoning about the joint posterior over model parameters. In this paper, we consider the use of factored representations combined with online planning techniques, to improve scalability of these methods...
July 2008: Uncertainty in Artificial Intelligence: Proceedings of the ... Conference
Laurel S Morris, Kwangyeol Baek, Prantik Kundu, Neil A Harrison, Michael J Frank, Valerie Voon
We focus on exploratory decisions across disorders of compulsivity, a potential dimensional construct for the classification of mental disorders. Behaviors associated with the pathological use of alcohol or food, in alcohol use disorders (AUD) or binge-eating disorder (BED), suggest a disturbance in explore-exploit decision-making, whereby strategic exploratory decisions in an attempt to improve long-term outcomes may diminish in favor of more repetitive or exploitatory choices. We compare exploration vs exploitation across disorders of natural (obesity with and without BED) and drug rewards (AUD)...
March 2016: Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology
Elizabeth Mannix, Margaret A Neale
-As the workplace has become increasingly diverse, there has been a tension between the promise and the reality of diversity in team process and performance. The optimistic view holds that diversity will lead to an increase in the variety of perspectives and approaches brought to a problem and to opportunities for knowledge sharing, and hence lead to greater creativity and quality of team performance. However, the preponderance of the evidence favors a more pessimistic view: that diversity creates social divisions, which in turn create negative performance outcomes for the group...
October 2005: Psychological Science in the Public Interest: a Journal of the American Psychological Society
Herman De Beukelaer, Geert De Meyer, Veerle Fack
BACKGROUND: Over the last decade genetic marker-based plant breeding strategies have gained increasing attention because genotyping technologies are no longer limiting. Now the challenge is to optimally use genetic markers in practical breeding schemes. For simple traits such as some disease resistances it is possible to target a fixed multi-locus allele configuration at a small number of causal or linked loci. Efficiently obtaining this genetic ideotype from a given set of parental genotypes is known as the marker-assisted gene pyramiding problem...
2015: BMC Genetics
Robert C Wilson, Andra Geana, John M White, Elliot A Ludvig, Jonathan D Cohen
All adaptive organisms face the fundamental tradeoff between pursuing a known reward (exploitation) and sampling lesser-known options in search of something better (exploration). Theory suggests at least two strategies for solving this dilemma: a directed strategy in which choices are explicitly biased toward information seeking, and a random strategy in which decision noise leads to exploration by chance. In this work we investigated the extent to which humans use these two strategies. In our "Horizon task," participants made explore-exploit decisions in two contexts that differed in the number of choices that they would make in the future (the time horizon)...
December 2014: Journal of Experimental Psychology. General
Andrew S Kayser, Jennifer M Mitchell, Dawn Weinstein, Michael J Frank
Whether to continue to exploit a source of reward, or to search for a new one of potentially greater value, is a fundamental and underconstrained decision. Recent computational studies of this exploration-exploitation tradeoff have found that variability in exploration across individuals is influenced by a functional polymorphism (Val158Met) in the catechol-O-methyltransferase (COMT) gene, whose protein product degrades synaptically released dopamine. However, these and other genotype-phenotype associations have rarely been causally tested...
January 2015: Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology
Morgan W B Kirzinger, Geetanchaly Nadarasah, John Stavrinides
Plant and human pathogens have evolved disease factors to successfully exploit their respective hosts. Phytopathogens utilize specific determinants that help to breach reinforced cell walls and manipulate plant physiology to facilitate the disease process, while human pathogens use determinants for exploiting mammalian physiology and overcoming highly developed adaptive immune responses. Emerging research, however, has highlighted the ability of seemingly dedicated human pathogens to cause plant disease, and specialized plant pathogens to cause human disease...
2011: Genes
Daniella Laureiro-Martínez, Nicola Canessa, Stefano Brusoni, Maurizio Zollo, Todd Hare, Federica Alemanno, Stefano F Cappa
An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time)...
2013: Frontiers in Human Neuroscience
Silvia García Díez, Jérôme Laforge, Marco Saerens
This paper proposes a simple extension of the celebrated MINIMAX algorithm used in zero-sum two-player games, called Rminimax. The Rminimax algorithm allows controlling the strength of an artificial rival by randomizing its strategy in an optimal way. In particular, the randomized shortest-path framework is applied for biasing the artificial intelligence (AI) adversary toward worse or better solutions, therefore controlling its strength. In other words, our model aims at introducing/implementing bounded rationality to the MINIMAX algorithm...
February 2013: IEEE Transactions on Cybernetics
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