Evolutionary Computation: An Emerging Framework for Practical Single and Multicriterion Optimization and Decision Making

2021 ◽  
pp. 255-286
Author(s):  
Kalyanmoy Deb
2014 ◽  
Vol 89 ◽  
pp. 118-125 ◽  
Author(s):  
M.E. Shafiee ◽  
A. Berglund ◽  
E. Zechman Berglund ◽  
E. Downey Brill ◽  
G. Mahinthakumar

2015 ◽  
Vol 21 (3) ◽  
pp. 379-393 ◽  
Author(s):  
Hiroki Sayama ◽  
Shelley D. Dionne

We report a summary of our interdisciplinary research project “Evolutionary Perspective on Collective Decision Making” that was conducted through close collaboration between computational, organizational, and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making, using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways—(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision-making processes, and (3) as a research tool for collecting high-resolution experimental data on actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.


2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.


2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.


2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.


2014 ◽  
Vol 38 (01) ◽  
pp. 46
Author(s):  
David R. Shanks ◽  
Ben R. Newell

2014 ◽  
Vol 38 (01) ◽  
pp. 48
Author(s):  
David R. Shanks ◽  
Ben R. Newell

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