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Found 16 papers, 0 papers with code
Solving Travelling Thief Problems using Coordination Based Methods
In TTP, city selection and item selection decisions need close coordination since the thief's travelling speed depends on the knapsack's weight and the order of visiting cities affects the order of item collection.
Deontic Meta-Rules
First, we introduce and formalise two variants of Defeasible Deontic Logic with Meta-Rules to represent (1) defeasible meta-theories with deontic modalities, and (2) two different types of conflicts among rules: Simple Conflict Defeasible Deontic Logic, and Cautious Conflict Defeasible Deontic Logic.
Surrogate Assisted Optimisation for Travelling Thief Problems
The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space.
A Cooperative Coordination Solver for Travelling Thief Problems
A thief performs a cyclic tour through a set of cities, and pursuant to a collection plan, collects a subset of items into a rented knapsack with finite capacity.
Toxicity Prediction by Multimodal Deep Learning
Prediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling.
Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees
Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources.
Dropout with Tabu Strategy for Regularizing Deep Neural Networks
Tabu Dropout has no extra parameters compared with the standard Dropout and also it is computationally cheap.
Machine Learning Interpretability: A Science rather than a tool
The term "interpretability" is oftenly used by machine learning researchers each with their own intuitive understanding of it.
Diversified Late Acceptance Search
The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome the main downside of traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration.
A New Approach for Revising Logic Programs
Belief revision has been studied mainly with respect to background logics that are monotonic in character.
Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction
Protein structure prediction is considered as one of the most challenging and computationally intractable combinatorial problem.
Exploiting Reduction Rules and Data Structures: Local Search for Minimum Vertex Cover in Massive Graphs
In this paper we propose a local search algorithm that exploits reduction rules and data structures to solve the MinVC problem in such graphs.
NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover
These two strategies are used in designing a new MVC local search algorithm, which is referred to as NuMVC.
Variable Forgetting in Reasoning about Knowledge
Given a background knowledge base and a set of observable variables for each agent, we show that the notion of an agent knowing a formula can be defined as a weakest sufficient condition of the formula under background knowledge base.
Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction
In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores.
A Hybrid Local Search for Simplified Protein Structure Prediction
Protein structure prediction based on Hydrophobic-Polar energy model essentially becomes searching for a conformation having a compact hydrophobic core at the center.
