Long Path Problems

  title={Long Path Problems},
  author={Jeffrey D. Horn and David E. Goldberg and Kalyanmoy Deb},
We demonstrate the interesting, counter-intuitive result that simple paths to the global optimum can be so long that climbing the path is intractable. This means that a unimodal search space, which consists of a single hill and in which each point in the space is on a simple path to the global optimum, can be difficult for a hillclimber to optimize. Various types of hillclimbing algorithms will make constant progress toward the global optimum on such long path problems. They will continuously… 
Genetic Algorithm Difficulty and the Modality of Fitness Landscapes
The function f mdG seems to be a powerful new tool for generalizing deception and relating hillclimbers (and Hamming space) to GAs and crossover and allows us to create functions, such as the minimum distance function fmdG, with k isolated global optima and multiple local optima attractive to both crossover and hillClimbers.
Genetic Algorithms, Problem Difficulty, and the Modality of Fitness Landscapes
This thesis addresses the appropriateness of Sewall Wright's tness landscape to the study of evolutionary computation, demonstrating that the metaphor can help us understand and predict the performance of the authors' algorithms, including both hillclimbers and recombinative GAs.
Genetic Algorithm Di culty and the Modality ofFitness
We assume that the modality (i.e., number of local optima) of a tness landscape is related to the diiculty of nding the best point on that landscape by evolutionary computation (e.g., hillclimbers
Analysis of recombinative algorithms on a non-separable building-block problem
  • R. Watson
  • Mathematics, Computer Science
  • 2000
An upper bound on the expected time for a recombinative algorithm to solve a nonseparable building-block problem by proving the existence of a path to the solution and calculating the time for each step on this path is given.
The analysis of evolutionary algorithms on sorting and shortest paths problems
This work analyzes simple EAs on well-known problems, namely sorting and shortest paths, and finds that sorting is the maximization of “sortedness” which is measured by one of several well- known measures of presortedness.
Heuristic algorithms and learning techniques: applications to the graph coloring problem
Taking the well-known graph coloring problem as an experimental framework, several new heuristics are developed that integrate certain learning mechanisms so as to render the search process more “self-aware”, including an algorithm that is able to record its trajectory and to interpret its own evolution.
Fitness Landscapes Based on Sorting and Shortest Paths Problems
Fitness landscapes based on important computer science problems as sorting and shortest paths problems are investigated here and it cannot be expected that evolutionary algorithms outperform the well-known problem specific algorithms on these simple problems.
Some Combinatorial Landscapes on which a Genetic Algorithm Outperforms Other Stochastic Iterative Methods
  • D. Corne, P. Ross
  • Computer Science
    Evolutionary Computing, AISB Workshop
  • 1995
This work shows one class of landscape arising from a class of real-world problems which fits the bill, and introduces a general class of landscapes which also display this ‘GA advantage’.
On the Effect of Connectedness for Biobjective Multiple and Long Path Problems
It is argued that connectedness is not the single property to study for the design of multiobjective local search algorithms, and opens new discussions on a proper definition of multi objective fitness landscapes.
Stepping stones and hidden haystacks: when a genetic algorithm defeats a hillclimber
  • D. Corne
  • Computer Science, Mathematics
    Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)
  • 1997
It is argued in particular that a specific notion of hillclimbing behaviour can with certain merits, and with certain qualifications, be included in this collection of descriptive tools with which to assess potential amenability to evolutionary search.


Reverse HillclimbingGenetic Algorithms and the Busy Beaver Problem
This paper introduces a new analysis tool called reverse hillclimbing, and demonstrates how it can be used to evaluate the performance of a genetic algorithm, using the {\it Busy Beaver problem}, an interesting problem of theoretical importance in its own right.
Relative Building-Block Fitness and the Building Block Hypothesis
A class of fitness landscapes (the “Royal Road” functions) that are designed to investigate the ability of the GA to produce fitter and fitter partial solutions by combining building blocks are described and some unexpected experimental results concerning the GA's performance on simple instances of these landscapes are presented.
When will a Genetic Algorithm Outperform Hill Climbing
An "idealized" genetic algorithm (IGA) is analyzed that is significantly faster than RMHC and that gives a lower bound for GA speed.
How Genetic Algorithms Really Work I.mutation and Hillclimbing
In this paper mutation and hillclimbing are analyzed with the help of representative binary functions and a simple asexual evolutionary algorithm. An optimal mutation rate is computed and a good
How Genetic Algorithms Really Work: Mutation and Hillclimbing
The invention relates to the production of washing powders of stabilized or enhanced appearance which contain a fluorescent whitening agent of the formula or of the formula wherein R1 is hydrogen or
Adaptation in natural and artificial systems
Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Genetic Algorithms in Search Optimization and Machine Learning
This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Theory of Error-correcting Codes
The field of channel coding started with Claude Shannon's 1948 landmark paper. Fifty years of efforts and invention have finally produced coding schemes that closely approach Shannon's channel
Difference-preserving codes
This paper discusses the application of DP codes to pattern recognition and classification problems and presents a construction of efficient DP codes whose information content is asymptotically of the order of theoretical upper bounds.
Making genetic algorithms y: a lesson from the Wright brothers
  • Advanced Technology for Developers. 2 February
  • 1993