Arc-consistency algorithms are widely used to prune the search-space of Constraint Satisfaction Problems (CSPs). They use support-checks (also known as consistency-checks) to find out about the properties of CSPs. They use arc-heuristics to select the next constraint and domain-heuristics to select the next values for their next support-check. We will investigate the effects of domain-heuristic...

For remote places having less-strong wind, single resources based renewable energy system (RES) with battery storage can sustainably and economically generate electrical energy. There is hardly any literature on optimal sizing of such RES for very low load demand situation. The objective of this study is to techno-economically optimize the system design of a Photovoltaic (PV)-battery storage RE...

GoTools is a program which solves life & death problems in the game of Go. This paper describes experiments using a Genetic Algorithm to optimize heuristic weights used by GoTools’ tree-search. The complete set of heuristic weights is composed of di erent subgroups, each of which can be optimized with a suitable tness function. As a useful side product, an MPI interface for FreePascal was imple...

Arc consistency algorithms are used in solving constraint satisfaction problems and are important in constraint logic programming languages. Search order heuristics for arc consistency algorithms signiicantly enhance the ef-ciency of their implementation. In this paper we propose and evaluate several ordering heuristics. Care is taken with experimental design , involving random problems, and st...

In this paper, we present several primal heuristics which we implemented in the branch-and-price solver GCG based on the SCIP framework. This involves new heuristics as well as heuristics from the literature that make use of the reformulation yielded by the Dantzig-Wolfe decomposition. We give short descriptions of those heuristics and briefly discuss computational results. Furthermore, we give...

This paper presents preliminary work on using deep neural networks to guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment. In more detail, we use learning in an attempt produce heuristics that can be used together with e.g., search generate feasible solutions of higher quality quickly. Our results indicate our approach could a promising future method c...