scholarly journals Exploration and Exploitation Zones in a Minimalist Swarm Optimiser

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 977
Author(s):  
Mohammad Majid al-Rifaie
Keyword(s):  
Inverse Problem ◽  
Tomographic Reconstruction ◽  
Stochastic Search ◽  
High Dimensional ◽  
Test Functions ◽  
Exploration And Exploitation ◽  
Trade Off ◽  
Exploration Exploitation

The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the population’s dimensional behaviour in each iteration and each defined exploration-exploitation zone, and to subsequently offer improvements to the working of the optimiser. The derived variants, titled unified DFO or uDFO, are successfully applied to an extensive set of test functions, as well as high-dimensional tomographic reconstruction, which is an important inverse problem in medical and industrial imaging.

Exploration and Exploitation in Parallel Problem Solving

2013 ◽  
pp. 201-215 ◽  
Author(s):  
Hua Zhang ◽  
Youmin Xi
Keyword(s):  
Problem Solving ◽  
Strategic Behavior ◽  
Group Performance ◽  
Performance Level ◽  
Exploration And Exploitation ◽  
Trade Off ◽  
Knowledge Process ◽  
Agent Behavior ◽  
Management Implication ◽  
Exploration Exploitation

In previous studies on coordinating exploration-exploitation activities, much attention has been paid on network structures while the roles played by actors’ strategic behavior have been largely ignored. In this paper, the authors extend March’s simulation model on parallel problem solving by adding structurally equivalent imitation. In this way, one can examine how the interaction of network structure with agent behavior affects the knowledge process and finally influence group performance. This simulation experiment suggests that under the condition of regular network, the classical trade-off between exploration and exploitation will appear in the case of the preferentially attached network when agents adopt structure equivalence imitation. The whole organization implicitly would be divided into independent sub-groups that converge on different performance level and lead the organization to a lower performance level. The authors also explored the performance in the mixed organization and the management implication.

scholarly journals Experience resetting in reinforcement learning facilitates exploration–exploitation transitions during a behavioral task for primates

2021 ◽  
Author(s):  
Kazuhiro Sakamoto ◽  
Hidetake Okuzaki ◽  
Akinori Sato ◽  
Hajime Mushiake
Keyword(s):  
Neural Network ◽  
Search Task ◽  
Fundamental Problem ◽  
Neural Mechanisms ◽  
Type Model ◽  
Behavioral Task ◽  
Exploration And Exploitation ◽  
Trade Off ◽  
Exploration Exploitation ◽  
Network Type

AbstractThe exploration–exploitation trade-off is a fundamental problem in re-inforcement learning. To study the neural mechanisms involved in this problem, a target search task in which exploration and exploitation phases appear alternately is useful. Monkeys well trained in this task clearly understand that they have entered the exploratory phase and quickly acquire new experiences by resetting their previous experiences. In this study, we used a simple model to show that experience resetting in the exploratory phase improves performance rather than decreasing the greediness of action selection, and we then present a neural network-type model enabling experience resetting.

Do new ventures explore, exploit or both? A case-based analysis of six innovative Italian start-ups

2019 ◽  
Vol 25 (7) ◽  
pp. 1515-1536 ◽  
Author(s):  
Pierluigi Rippa ◽  
Cristina Ponsiglione ◽  
Anca Bocanet ◽  
Guido Capaldo ◽  
Giuseppe Zollo
Keyword(s):  
New Ventures ◽  
New Venture ◽  
Venture Capitalists ◽  
Exploration And Exploitation ◽  
Temporal Separation ◽  
Trade Off ◽  
Content Type ◽  
Start Ups ◽  
Exploration Exploitation ◽  
The Way

Purpose The purpose of this paper is to contribute to the debate on exploration–exploitation trade-off in the context of new ventures creation, where, particularly at the empirical level, there is a limited understanding of whether and how this trade-off is achieved and how start-ups performances are affected by the way in which they face the exploration–exploitation dilemma. Design/methodology/approach A qualitative case study approach has been adopted as a methodology to conduct the research. Six Italian innovative start-ups were selected and analyzed through in-depth interviews with founders and data collection to understand whether and how start-ups adopt exploration and exploitation solutions to face critical events in their business lives. Findings The most evident result of this study is that start-ups adopt more frequently a temporal separation of exploration and exploitation activities as the preferred mode for balancing learning and innovation tension. They do not seem to exhibit a defined or a common path in the way they realize the temporal separation between exploration and exploitation. Instead, they mostly oscillate. The ambidextrous solution is selected in only a few cases and not consecutively. The pre-entry knowledge profile seems to influence the choice of start-ups at the beginning of their lives. Practical implications This research has implications for the whole start-up’s ecosystem, comprising incubators/accelerators, advisors, intermediaries, venture capitalists, new venture founders and policymakers. For example, by knowing the typology of knowledge and competence gaps start-ups usually aim to fill when they face particular events, intermediaries (such as incubators) could better plan initiatives and strategies supporting new ventures in the process of growth and stabilization. Furthermore, the venture capitalists can benefit from this research, by planning specific interventions for each critical event based on specific resources and competencies gaps and guiding for more promising start-ups. Originality/value This paper presents a novel application of entrepreneurial learning approach in the context of new venture creation. To reach this aim, a classification of exploration/exploitation solutions has been developed.

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

2021 ◽  
pp. 1-12
Author(s):  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
High Dimensional ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

scholarly journals Understanding Managers’ Trade-Offs Between Exploration and Exploitation

2021 ◽  
Author(s):  
Alina Ferecatu ◽  
Arnaud De Bruyn
Keyword(s):  
Learning Model ◽  
Decision Makers ◽  
Exploration And Exploitation ◽  
Trade Offs ◽  
Exploration Exploitation

This paper develops a learning model to describe decision makers' exploration/exploitation trade-offs and their link to psychometric traits.

scholarly journals Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

2020 ◽  
Author(s):  
Julian Berk ◽  
Sunil Gupta ◽  
Santu Rana ◽  
Svetha Venkatesh
Keyword(s):  
Gaussian Process ◽  
Real World ◽  
Confidence Bound ◽  
Trade Off ◽  
Upper Confidence Bound ◽  
Exploration Exploitation

In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.

scholarly journals Systematic Exploration and Uncertainty Dominate Young Children’s Choices

2019 ◽  
Author(s):  
Nathaniel J. Blanco ◽  
Vladimir Sloutsky
Keyword(s):  
Decision Making ◽  
Young Children ◽  
Cognitive Control ◽  
Exploration And Exploitation ◽  
Top Down ◽  
Reward Seeking ◽  
Systematic Fashion ◽  
Systematic Exploration ◽  
Competing Demands ◽  
Exploration Exploitation

Organisms need to constantly balance the competing demands of gathering information and using previously acquired information to obtain rewarding outcomes (i.e., the “exploration- exploitation” dilemma). Exploration is critical to obtain information to discover how the world works, which should be particularly important for young children. While studies have shown that young children explore in response to surprising events, little is known about how they balance exploration and exploitation across multiple decisions or about how this process changes with development. In this study we compare decision-making patterns of children and adults and evaluate the relative influences of reward-seeking, random exploration, and systematic switching (which approximates uncertainty-directed exploration). In a second experiment we directly test the effect of uncertainty on children’s choices. Influential models of decision-making generally describe systematic exploration as a computationally refined capacity that relies on top-down cognitive control. We demonstrate that (1) systematic patterns dominate young children’s behavior (facilitating exploration), despite protracted development of cognitive control, and (2) that uncertainty plays a major, but complicated, role in determining children’s choices. We conclude that while young children’s immature top-down control should hinder adult-like systematic exploration, other mechanisms may pick up the slack, facilitating broad information gathering in a systematic fashion to build a foundation of knowledge for use later in life.

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