Evolutionary algorithms are problem-independent randomized search heuristics. It is discussed when it is useful to work with such algorithms and it is argued why these search heuristics should be analyzed just as all other deterministic and randomized algorithms. Such an approach is started by analyzing a simple evolutionary algorithm on linear functions, quadratic functions, unimodal functions...

Randomized search heuristics (e.g., evolutionary algorithms, simulated annealing etc.) are very appealing to practitioners, they are easy to implement and usually provide good performance. The theoretical analysis of these algorithms usually focuses on convergence rates. This paper presents a mathematical study of randomized search heuristics which use comparison based selection mechanism. The ...

We report on careful implementations of several push-relabel-based algorithms for solving the problem of finding a maximum cardinality matching in a bipartite graph and compare them with fast augmentingpath-based algorithms. We analyze the algorithms using a common base for all implementations and compare their relative performance and stability on a wide range of graphs. The effect of a set of...

Optimal path planning is the problem of finding a valid sequence states between start and goal that optimizes an objective. Informed algorithms order their search with problem-specific knowledge expressed as heuristics can be orders magnitude more efficient than uninformed algorithms. Heuristics are most effective when they both accurate computationally inexpensive to evaluate, but these often ...

We propose a general approach to modeling semisupervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling both traditional supervised classification tasks and many SSL heuristics, including both well-known heuristics such as co-training and novel domainspecific heuristics. In addition to representing individual SSL heuristics, we show that multiple heurist...

Most scheduling problems are notoriously intractable, so the majority of algorithms for them are heuristic in nature. Priority rule-based methods still constitute the most important class of these heuristics. Of these, in turn, parameterized biased random sampling methods have attracted particular interest, due to the fact that they outperform all other priority rule-based methods known. Yet, e...

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic ...

Suboptimal search algorithms offer shorter solving times by sacrificing guaranteed solution optimality. While optimal search algorithms like A* and IDA* require admissible heuristics, suboptimal search algorithms need not constrain their guidance in this way. Previous work has explored using off-line training to transform admissible heuristics into more effective inadmissible ones. In this pape...

We present heuristics and fast approximate algorithms for NP-complete combinatorial optimization problems in binary variables. These heuristics are inspired from gradient ascents in a randomized phase space. In good cases a formula of (Russo, 1981) and a theorem of Lindeberg allow us to estimate the dynamics and derive algorithms. As an application we present a new approximate algorithm for the...

This paper furthers the recent investigation of search heuristics based on solution counting information, by proposing and evaluating algorithms to compute solution densities of variable-value pairs in knapsack constraints. Given a domain consistent constraint, our first algorithm is inspired from what was proposed for regular language membership constraints. Given a bounds consistent constrain...