91 research outputs found
Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms
The fitness level method is a popular tool for analyzing the computation time
of elitist evolutionary algorithms. Its idea is to divide the search space into
multiple fitness levels and estimate lower and upper bounds on the computation
time using transition probabilities between fitness levels. However, the lower
bound generated from this method is often not tight. To improve the lower
bound, this paper rigorously studies an open question about the fitness level
method: what are the tightest lower and upper time bounds that can be
constructed based on fitness levels? To answer this question, drift analysis
with fitness levels is developed, and the tightest bound problem is formulated
as a constrained multi-objective optimization problem subject to fitness level
constraints. The tightest metric bounds from fitness levels are constructed and
proven for the first time. Then the metric bounds are converted into linear
bounds, where existing linear bounds are special cases. This paper establishes
a general framework that can cover various linear bounds from trivial to best
coefficients. It is generic and promising, as it can be used not only to draw
the same bounds as existing ones, but also to draw tighter bounds, especially
on fitness landscapes where shortcuts exist. This is demonstrated in the case
study of the (1+1) EA maximizing the TwoPath function
A comparative runtime analysis of heuristic algorithms for satisfiability problems
AbstractThe satisfiability problem is a basic core NP-complete problem. In recent years, a lot of heuristic algorithms have been developed to solve this problem, and many experiments have evaluated and compared the performance of different heuristic algorithms. However, rigorous theoretical analysis and comparison are rare. This paper analyzes and compares the expected runtime of three basic heuristic algorithms: RandomWalk, (1+1) EA, and hybrid algorithm. The runtime analysis of these heuristic algorithms on two 2-SAT instances shows that the expected runtime of these heuristic algorithms can be exponential time or polynomial time. Furthermore, these heuristic algorithms have their own advantages and disadvantages in solving different SAT instances. It also demonstrates that the expected runtime upper bound of RandomWalk on arbitrary k-SAT (k⩾3) is O((k−1)n), and presents a k-SAT instance that has Θ((k−1)n) expected runtime bound
A Comparison of GAs Penalizing Infeasible Solutions and Repairing Infeasible Solutions on the 0-1 Knapsack Problem
Constraints exist in almost every optimization problem. Different
constraint handling techniques have been incorporated with genetic
algorithms (GAs), however most of current studies are based on
computer experiments. An example is Michalewicz\u27s comparison among
GAs using different constraint handling techniques on the 0-1
knapsack problem. The following phenomena are observed in
experiments: 1) the penalty method needs more generations to find a
feasible solution to the restrictive capacity knapsack than the
repair method; 2) the penalty method can find
better solutions to the average capacity knapsack. Such observations
need a theoretical explanation. This paper aims at providing a
theoretical analysis of Michalewicz\u27s experiments. The main result
of the paper is that GAs using the repair method are more efficient
than GAs using the penalty method on both restrictive capacity and
average capacity knapsack problems. This result of the average
capacity is a little different from Michalewicz\u27s experimental
results. So a supplemental experiment is implemented to support the
theoretical claim. The results confirm the general principle pointed
out by Coello: a better constraint-handling approach should tend to
exploit specific domain knowledge
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of
knowledge graph completion (KGC) models have been proposed to predict missing
links from known triples. Among those, several works have achieved more
advanced results via exploiting the structure information on KGs with Graph
Convolutional Networks (GCN). However, we observe that entity embeddings
aggregated from neighbors in different directions are just simply averaged to
complete single-tasks by existing GCN based models, ignoring the specific
requirements of forward and backward sub-tasks. In this paper, we propose a
Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction
information, the multi-head self-attention is applied to specifically combine
embeddings in different directions based on various entities and sub-tasks, the
geometric constraints are imposed to adjust the distribution of embeddings, and
the traditional binary cross-entropy loss is modified to reflect the triple
uncertainty. Moreover, the competitive experiments results on several benchmark
datasets verify the effectiveness of our model
Performance Analysis of Evolutionary Algorithms for the Minimum Label Spanning Tree Problem
Some experimental investigations have shown that evolutionary algorithms
(EAs) are efficient for the minimum label spanning tree (MLST) problem.
However, we know little about that in theory. As one step towards this issue,
we theoretically analyze the performances of the (1+1) EA, a simple version of
EAs, and a multi-objective evolutionary algorithm called GSEMO on the MLST
problem. We reveal that for the MLST problem the (1+1) EA and GSEMO
achieve a -approximation ratio in expected polynomial times of
the number of nodes and the number of labels. We also show that GSEMO
achieves a -approximation ratio for the MLST problem in expected
polynomial time of and . At the same time, we show that the (1+1) EA and
GSEMO outperform local search algorithms on three instances of the MLST
problem. We also construct an instance on which GSEMO outperforms the (1+1) EA
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