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Experimental evolution has yielded surprising insights into human history and evolution by shedding light on the roles of chance and contingency in history and evolution, and on the deep evolutionary roots of cooperation, conflict and kin…

Populations and Evolution · Quantitative Biology 2018-10-02 Rohan Maddamsetti , Jacob Bower-Bir

Genetic Algorithms have established their capability for solving many complex optimization problems. Even as good solutions are produced, the user's understanding of a problem is not necessarily improved, which can lead to a lack of…

Neural and Evolutionary Computing · Computer Science 2024-07-10 GianCarlo Catalano , Alexander E. I. Brownlee , David Cairns , John McCall , Russell Ainslie

The human nervous system utilizes synaptic plasticity to solve optimization problems. Previous studies have tried to add the plasticity factor to the training process of artificial neural networks, but most of those models require complex…

Neural and Evolutionary Computing · Computer Science 2022-04-13 Amir Valizadeh

In the past 30 years, scientists have searched nature, including animals and insects, and biology in order to discover, understand, and model solutions for solving large-scale science challenges. The study of bionics reveals that how the…

Neural and Evolutionary Computing · Computer Science 2022-02-09 Farid Ghareh Mohammadi , Farzan Shenavarmasouleh , Khaled Rasheed , Thiab Taha , M. Hadi Amini , Hamid R. Arabnia

Biology is data-rich, and it is equally rich in concepts and hypotheses. Part of trying to understand biological processes and systems is therefore to confront our ideas and hypotheses with data using statistical methods to determine the…

Quantitative Methods · Quantitative Biology 2022-06-22 Sean T. Vittadello , Michael P. H. Stumpf

Eudaemonics is the study of the nature, causes, and conditions of human well-being. According to the ethical theory of eudaemonia, reaping satisfaction and fulfillment from life is not only a desirable end, but a moral responsibility.…

Computers and Society · Computer Science 2009-05-13 Marko A. Rodriguez , Jennifer H. Watkins

We present a theoretical as well as experimental investigation of a population of self-replicating segments of code subject to random mutation and survival of the fittest. Under the assumption that such a system constitutes a minimal system…

adap-org · Physics 2008-02-03 Chris Adami

Why are life trajectories difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multi-decade longitudinal study. Our sampling and interviewing process were informed by…

The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix…

Machine Learning · Computer Science 2021-02-02 Thibaut Vidal , Toni Pacheco , Maximilian Schiffer

As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging for policymakers and researchers. We introduce a methodology…

Computers and Society · Computer Science 2026-05-28 Julia Barnett , Kimon Kieslich , Natali Helberger , Nicholas Diakopoulos

Many biological processes have been the source of inspiration for heuristic methods that generate high-quality solutions to solve optimization and search problems. This thesis presents an epigenetic technique for Evolutionary Algorithms,…

Neural and Evolutionary Computing · Computer Science 2021-02-22 Alvarez Lifeth

One of the problems in applying Genetic Algorithm is that there is some situation where the evolutionary process converges too fast to a solution which causes it to be trapped in local optima. To overcome this problem, a proper diversity in…

Neural and Evolutionary Computing · Computer Science 2011-09-02 Chaiwat Jassadapakorn , Prabhas Chongstitvatana

Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this…

Artificial Intelligence · Computer Science 2023-03-27 Ruqing Xu , Sarah Dean

Nature features a plethora of extraordinary photonic architectures that have been optimized through natural evolution. While numerical optimization is increasingly and successfully used in photonics, it has yet to replicate any of these…

Evolution is a fundamental process that shapes the biological world we inhabit, and reinforcement learning is a powerful tool used in artificial intelligence to develop intelligent agents that learn from their environment. In recent years,…

Neural and Evolutionary Computing · Computer Science 2023-06-19 Taboubi Ahmed

In spite of their seemingly "obvious" virtues as a search strategy, genetic algorithms have ended up playing only a modest role as design tools in science and engineering. We review the reasons for this apparent failure, and we suggest a…

Adaptation and Self-Organizing Systems · Physics 2007-05-23 Tommaso Toffoli

Research in Artificial Intelligence is breaking technology barriers every day. New algorithms and high performance computing are making things possible which we could only have imagined earlier. Though the enhancements in AI are making life…

The field of engineering is shaped by the tools and methods used to solve problems. Optimization is one such class of powerful, robust, and effective engineering tools proven over decades of use. Within just a few years, generative…

Computational Engineering, Finance, and Science · Computer Science 2025-12-02 Lyle Regenwetter , Cyril Picard , Amin Heyrani Nobari , Akash Srivastava , Faez Ahmed

Online genealogy datasets contain extensive information about millions of people and their past and present family connections. This vast amount of data can assist in identifying various patterns in human population. In this study, we…

Social and Information Networks · Computer Science 2014-01-07 Michael Fire , Yuval Elovici

This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger…

Neural and Evolutionary Computing · Computer Science 2010-07-05 Uwe Aickelin , Larry Bull
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