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On the Impossibility of Surviving (Iterated) Deletion of Weakly Dominated Strategies in Rational MPC
Rational multiparty computation (rational MPC) provides a framework for analyzing MPC protocols through the lens of game theory. One way to judge...
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Playing Repeated Coopetitive Polymatrix Games with Small Manipulation Cost
Repeated coopetitive games capture the situation when one must efficiently balance between cooperation and competition with the other agents over...
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An EEG-based subject-independent emotion recognition model using a differential-evolution-based feature selection algorithm
Electroencephalogram (EEG)-based emotion recognition models are gaining interest as they show the intrinsic state of human. A wide range of features...
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Safe Pareto improvements for delegated game playing
A set of players delegate playing a game to a set of representatives, one for each player. We imagine that each player trusts their respective...
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Event-triggered multi-agent credit allocation pursuit-evasion algorithm
The reinforcement learning is used to study the problem of multi-agent pursuit-evasion games in this article. The main problem of current...
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Evolutionary Successful Strategies in a Transparent iterated Prisoner’s Dilemma
A Transparent game is a game-theoretic setting that takes action visibility into account. In each round, depending on the relative timing of their...
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Sorting
Sorting a sequence of n elements probably is the most fascinating topic in computer science, and improved sorting implementations have significant...
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An improved master-apprentice evolutionary algorithm for minimum independent dominating set problem
The minimum independent dominance set (MIDS) problem is an important version of the dominating set with some other applications. In this work, we...
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An empirical characterization of community structures in complex networks using a bivariate map of quality metrics
Community detection emerges as an important task in the discovery of network mesoscopic structures. However, the concept of a “good” community is...
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Towards Constructing a Suite of Multi-objective Optimization Problems with Diverse Landscapes
Given that real-world multi-objective optimization problems are generally constructed by combining individual functions to be optimized, it seems...
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A model-based many-objective evolutionary algorithm with multiple reference vectors
In order to estimate the Pareto front, most of the existing evolutionary algorithms apply the discovery of non-dominated solutions in search space,...
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A hybrid bi-objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach
Heterogeneous cloud datacenters are well-suited and cost-efficient platforms for execution of scientific workflows requested from academics. Workflow...
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An Efficient Hybrid Mine Blast Algorithm for Tackling Software Fault Prediction Problem
An inherent problem in software engineering is that competing prediction systems have been found to produce conflicting results. Yet accurate...
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Online-learning task scheduling with GNN-RL scheduler in collaborative edge computing
With the development of collaborative edge computing (CEC), the manufacturing market is gradually moving toward large-scale, multi-scenario, and...
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Analysing Multiobjective Optimization Using Evolutionary Path Length Correlation
Recently, a number of studies have attempted to characterize the interaction between objectives and decision variables in multiobjective problems. In...
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Wrapper-based optimized feature selection using nature-inspired algorithms
Computations that mimic nature are known as nature-inspired computing. Nature presents a wealthy source of thoughts and ideas for computing. The use...
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Towards a Theory of Valid Inferential Models with Partial Prior Information
Inferential models (IMs) are used to quantify uncertainty in statistical inference problems, and validity is a crucial property that ensures the IM’s...
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What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning
It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its...
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An integrated approach using IF-TOPSIS, fuzzy DEMATEL, and enhanced CSA optimized ANFIS for software risk prediction
Successful project is determined based on its effective performance and prioritization of all unavoidable software project risks. In this paper, the...
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Learning Beam Search: Utilizing Machine Learning to Guide Beam Search for Solving Combinatorial Optimization Problems
Beam search (BS) is a well-known incomplete breadth-first-search variant frequently used to find heuristic solutions to hard combinatorial...