Convergence of a hill-climbing genetic algorithm for graph matching
نویسندگان
چکیده
منابع مشابه
Convergence of a Hill Climbing Genetic Algorithm for Graph Matching
This paper presents a convergence analysis for the problem of consistent labelling using genetic search. The work builds on a recent empirical study of graph matching where we showed that a Bayesian consistency measure could be e$ciently optimised using a hybrid genetic search procedure which incorporated a hill-climbing step. In the present study we return to the algorithm and provide some the...
متن کاملWhen a genetic algorithm outperforms hill-climbing
A toy optimisation problem is introduced which consists of a ÿtness gradient broken up by a series of hurdles. The performance of a hill-climber and a stochastic hill-climber are computed. These are compared with the empirically observed performance of a genetic algorithm (GA) with and without. The hill-climber with a suuciently large neighbourhood outperforms the stochastic hill-climber, but i...
متن کاملIntegrated Genetic Algorithm with Hill Climbing for Bandwidth Minimization Problem
In this paper, we propose an integrated Genetic Algorithm with Hill Climbing to solve the matrix bandwidth minimization problem, which is to reduce bandwidth by permuting rows and columns resulting in the nonzero elements residing in a band as close as possible to the diagonal. Experiments show that this approach achieves the best solution quality when compared with the GPS [1] algorithm, Tabu ...
متن کاملWhen Will a Genetic Algorithm Outperform Hill Climbing?
In this paper we review some previously published experimental results in which a simple hillclimbing algorithm-Random Mutation Hill-Climbing (RMHC)-significantly outperforms a genetic algorithm on a simple "Royal Road" function. vVe present an analysis of RMHC followed by an analysis of an "idealized" genetic algorithm (IGA) that is in turn significantly faster than RMHC. We isolate the featur...
متن کاملPALO: A Probabilistic Hill-Climbing Algorithm
Many learning systems search through a space of possible performance elements, seeking an element whose expected utility, over the distribution of problems, is high. As the task of nding the globally optimal element is often intractable, many practical learning systems instead hill-climb to a local optimum. Unfortunately, even this is problematic as the learner typically does not know the under...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2000
ISSN: 0031-3203
DOI: 10.1016/s0031-3203(99)00171-5