The design and analysis of problem-specific algorithms for combinatorial optimization problems is a well-studied subject. It is accepted that randomization is a powerful concept for theoretically and practically efficient problem-specific algorithms. Randomized search heuristics like randomized local search, tabu search, simulated annealing, and variants of evolutionary algorithms can be combin...

In the previous chapters of this book, we have seen techniques for solving NP-hard combinatorial optimization problems exactly, i.e. methods that nd a solution which is guaranteed to be optimal. These methods carry out an implicit search through the entire space of solutions, thereby guaranteeing the best solution found during this process to be an optimal solution. The price paid for this guar...

We explore neural network and related heuristic methods for the fast approximate solution of the Maximum Clique problem. One of these algorithms, Mean Field Annealing, is implemented on the Connection Machine CM-5 and a fast annealing schedule is experimentally evaluated on random graphs, as well as on several benchmark graphs. The other algorithms, which perform certain randomized local search...

We examine probabilistic greedy heuristics for maximization and minimization versions of the satisfiability problem. Like deterministic greedy algorithms, these heuristics construct a truth assignment one variable at a time. Unlike them, they set a variable true or false using a probabilistic mechanism, the probabilities of a true assignment depending on the incremental number of clauses satisf...

This report documents the talks and discussions at the Dagstuhl Seminar 15211 “Theory of Evolutionary Algorithms”. This seminar, now in its 8th edition, is the main meeting point of the highly active theory of randomized search heuristics subcommunities in Australia, Asia, North America, and Europe. Topics intensively discussed include rigorous runtime analysis and computational complexity theo...

This paper deals with the study of the exchange of messages among a set of processors linked through an interconnection network. We focus on general, non-uniform versions of message exchange problems in asynchronous systems with a linear cost model and messages of arbitrary sizes. We extend previous complexity results to show that the general asynchronous problems are NP-complete. We present se...

In this paper, human heuristics have been identified that provide close to optimal solutions when solving Capacitated Vehicle Routing Problems (CVRPs). Results from previous experiments showed humans can produce good solutions relatively fast that compete with computer-based methods giving further support to previous research on Traveling Salesman Problems (TSPs). Multiple Regression analyses h...

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for the multi-agent pathfinding problem. However, existing variants of CBS do not use any heuristics that estimate future work. In this paper, we introduce different admissible heuristics for CBS by aggregating cardinal conflicts among agents. In our experiments, CBS with these heuristics outperforms previous st...

The design and development of network infrastructure to support mission-critical operations has become a critical and complicated issue. In this study we explore the use of genetic algorithms (GA) for the design of a degree constrained minimal spanning tree (DCMST) problem with varied degrees on each node. The performance of GA was compared with two popular heuristics. The results indicate that...

We study a network model in which each network link is associated with a set of delays and costs. These costs are a function of the delays and reflect the prices paid in return for delay guarantees. Such a cost structure can model a setting in which the service provider provides multiple service classes with a different price and delay guarantee for each class. We are given a source node s, a s...