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Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interesting tool for interpretable machine learning. It has been…

Machine Learning · Computer Science 2021-06-28 Jonathan Spieler , Nico Potyka , Steffen Staab

The $(1+(\lambda,\lambda))$ genetic algorithm is a bright example of an evolutionary algorithm which was developed based on the insights from theoretical findings. This algorithm uses crossover, and it was shown to asymptotically outperform…

Neural and Evolutionary Computing · Computer Science 2020-05-12 Anton Bassin , Maxim Buzdalov

Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them…

Neural and Evolutionary Computing · Computer Science 2016-07-20 Abhishek Gupta , Yew-Soon Ong

Universal induction relies on some general search procedure that is doomed to be inefficient. One possibility to achieve both generality and efficiency is to specialize this procedure w.r.t. any given narrow task. However, complete…

Neural and Evolutionary Computing · Computer Science 2018-09-13 Alexey Potapov , Sergey Rodionov

The purpose of this paper is to give an introduction to the field of Schema Theory written by a mathematician and for mathematicians. In particular, we endeavor to to highlight areas of the field which might be of interest to a…

Neural and Evolutionary Computing · Computer Science 2021-09-01 David White

Testing provides means pertaining to assuring software performance. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. However, associated with software testing has…

Software Engineering · Computer Science 2016-12-30 Ahmed Mateen , Marriam Nazir , Salman Afsar Awan

Autonomous spacecraft maneuver planning using an evolutionary algorithmic approach is investigated. Simulated spacecraft were placed into four different initial orbits. Each was allowed a string of thirty delta-v impulse maneuvers in six…

Neural and Evolutionary Computing · Computer Science 2020-11-17 Sidhdharth Sikka , Harshvardhan Sikka

This paper presents a method for large corpus analysis to semantically classify an entire clause. In particular, we use cooccurrence statistics among similar clauses to determine the aspectual class of an input clause. The process examines…

cmp-lg · Computer Science 2008-02-03 Eric V. Siegel , Kathleen R. McKeown

These lecture notes introduce key concepts of mathematical population genetics within the most elementary setting and describe a few recent applications to microbial evolution experiments. Pointers to the literature for further reading are…

Populations and Evolution · Quantitative Biology 2018-03-23 Joachim Krug

Here a genetic algorithm (GA) is presented that creates a teaching schedule for a university physics department by algorithmically assigning ${\sim}200$ classes to ${\sim}50$ professors for each of three academic terms per year. The…

Neural and Evolutionary Computing · Computer Science 2025-09-10 Tom Bensky , Karl Saunders

Distributed quantum computing has been well-known for many years as a system composed of a number of small-capacity quantum circuits. Limitations in the capacity of monolithic quantum computing systems can be overcome by using distributed…

Many real-world optimization problems occur in environments that change dynamically or involve stochastic components. Evolutionary algorithms and other bio-inspired algorithms have been widely applied to dynamic and stochastic problems.…

Neural and Evolutionary Computing · Computer Science 2020-01-30 Vahid Roostapour , Mojgan Pourhassan , Frank Neumann

An important question in evolutionary computation is how good solutions evolutionary algorithms can produce. This paper aims to provide an analytic analysis of solution quality in terms of the relative approximation error, which is defined…

Neural and Evolutionary Computing · Computer Science 2016-11-28 Jun He

Evolutionary algorithms rely very heavily on randomized behavior. Execution speed, therefore, depends strongly on how we implement randomness, such as our choice of pseudorandom number generator, or the algorithms used to map pseudorandom…

Neural and Evolutionary Computing · Computer Science 2024-12-04 Vincent A. Cicirello

In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty quantification and (ii) rule-based systems for the…

Machine Learning · Statistics 2022-03-01 Themistoklis Botsas , Lachlan R. Mason , Omar K. Matar , Indranil Pan

Cellular automata are discrete and computational models thatcan be shown as general models of complexity. They are used in varied applications to derive the generalized behavior of the presented model. In this paper we have took one such…

Neural and Evolutionary Computing · Computer Science 2020-05-14 Karan Nayak

This article presents results of experimental studies the effectiveness of the genetic algorithm that was applied to effective queries creation and relevant document selection. Studies were carried out to the comparative analysis of the…

Information Retrieval · Computer Science 2015-04-06 V. K. Ivanov , B. V. Palyukh

This work was inspired by author experiences with a telescope scheduling. Author long time goal is to develop and further extend software for an autonomous observatory. The software shall provide users with all the facilities they need to…

Artificial Intelligence · Computer Science 2010-02-02 Petr Kubanek

Reversible Cellular Automata (RCA) are a particular kind of shift-invariant transformations characterized by a dynamics composed only of disjoint cycles. They have many applications in the simulation of physical systems, cryptography and…

Neural and Evolutionary Computing · Computer Science 2021-05-26 Luca Mariot , Stjepan Picek , Domagoj Jakobovic , Alberto Leporati

Coevolution is a powerful tool in evolutionary computing that mitigates some of its endemic problems, namely stagnation in local optima and lack of convergence in high dimensionality problems. Since its inception in 1990, there are multiple…

Neural and Evolutionary Computing · Computer Science 2015-06-30 Noe Casas
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