Changing Representations During Search: A Comparative Study of Delta Coding

1994 ◽  
Vol 2 (3) ◽  
pp. 249-278 ◽  
Author(s):  
Keith E. Mathias ◽  
L. Darrell Whitley
Keyword(s):  
Comparative Study ◽  
Search Strategy ◽  
Search Space ◽  
Hill Climbing ◽  
Test Functions ◽  
Asymptotic Performance ◽  
Random Mutation ◽  
Genetic Search ◽  
Problem Representations

Delta coding is an iterative genetic search strategy that dynamically changes the representation of the search space in an attempt to exploit different problem representations. Delta coding sustains search by reinitializing the population at each iteration of search. This helps to avoid the asymptotic performance typically observed in genetic search as the population becomes more homogeneous. Here, the optimization ability of delta coding is empirically compared against CHC, ESGA, GENITOR, and random mutation hill-climbing (RMHC) on a suite of well-known test functions with and without Gray coding. Issues concerning the effects of Gray coding on these test functions are addressed.

scholarly journals The FF Planning System: Fast Plan Generation Through Heuristic Search

2001 ◽  
Vol 14 ◽  
pp. 253-302 ◽  
Author(s):  
J. Hoffmann ◽  
B. Nebel
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Search Strategy ◽  
Search Space ◽  
Systematic Search ◽  
Hill Climbing ◽  
Planning System ◽  
Plan Generation ◽  
State Space Search ◽  
Algorithmic Techniques

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

scholarly journals Machine-coded genetic operators and their performances in floating-point genetic algorithms

2017 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Mehmet Hakan Satman ◽  
Emre Akadal
Keyword(s):  
Genetic Algorithms ◽  
Search Space ◽  
Fine Tuning ◽  
Floating Point ◽  
Mutation Operator ◽  
Genetic Operators ◽  
Random Mutation ◽  
Genetic Search ◽  
Decision Variables ◽  
New Type

Machine-coded genetic algorithms (MCGAs) use the byte representation of floating-point numbers which are encoded in the computer memory. Use of the byte alphabet makes classical crossover operators directly applicable in the floating-point genetic algorithms. Since effect of the byte-based mutation operator depends on the location of the mutated byte, the byte-based mutation operator mimics the functionality of its binary counterpart. In this paper, we extend the MCGA by developing new type of byte-based genetic operators including a random mutation and a random dynamic mutation operator. We perform a simulation study to compare the performances of the byte-based operators with the classical FPGA operators using a set of test functions. The prepared software package, which is freely available for downloading, is used for the simulations. It is shown that the byte-based genetic search obtains precise results by carrying out the both exploration and exploitation tasks by discovering new fields of the search space and performing a local fine-tuning. It is also shown that the introduced byte-based operators improve the search capabilities of FPGAs by means of convergence rate and precision even if the decision variables are in larger domains.

scholarly journals RMHC-MR: Instance selection by random mutation hill climbing algorithm with MapReduce in big data

2017 ◽  
Vol 111 ◽  
pp. 252-259 ◽  
Author(s):  
Lu Si ◽  
Jie Yu ◽  
Wuyang Wu ◽  
Jun Ma ◽  
Qingbo Wu ◽  
...  
Keyword(s):  
Big Data ◽  
Hill Climbing ◽  
Instance Selection ◽  
Random Mutation ◽  
Hill Climbing Algorithm

Global search in single-solution-based metaheuristics

2020 ◽  
Vol 54 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Najmeh Sadat Jaddi ◽  
Salwani Abdullah
Keyword(s):  
Search Space ◽  
Population Based ◽  
Global Optimum ◽  
Global Search ◽  
Hill Climbing ◽  
Search Process ◽  
Local Optimum ◽  
Content Type ◽  
Solution Algorithms ◽  
Single Solution

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

External Knowledge Search Strategy as an Essential Element of a Knowledge Management Strategy

2011 ◽  
pp. 70-89
Author(s):  
Fergal McGrath ◽  
Rebecca Purcell
Keyword(s):  
Knowledge Management ◽  
Management Strategy ◽  
Search Strategy ◽  
Central Element ◽  
Essential Element ◽  
Search Space ◽  
Knowledge Management Strategy ◽  
External Knowledge ◽  
Knowledge Search ◽  
The Many

This chapter introduces external knowledge search strategy as a central element of an organizations overall knowledge management strategy. The argument cites how knowledge management has developed around a myopic internal focus and has thus far failed to take full account of the many sources of knowledge external to the organization. The chapter offers external knowledge search strategy as a means of integrating this external focus into knowledge management understanding, by providing a conceptual framework for organizations involved in the external knowledge management activity of external knowledge search. The framework identifies 10 search paths organizations may follow into the search space, four of which relate exclusively to external knowledge search. The authors hope that establishing an external element within knowledge management strategy will inform knowledge management’s recognition of the value of the extended enterprise.

scholarly journals A Tabu search method for the optimization of fluid power circuits

1998 ◽  
Vol 212 (5) ◽  
pp. 373-381 ◽  
Author(s):  
A M Connor ◽  
D G Tilley
Keyword(s):  
Tabu Search ◽  
Simulation Software ◽  
Search Method ◽  
Fluid Power ◽  
Hill Climbing ◽  
Test Functions ◽  
Local Optima ◽  
Power Circuits ◽  
Tabu Search Method ◽  
Optimum Solution

This paper describes the development of an efficient algorithm for the optimization of fluid power circuits. The algorithm is based around the concepts of Tabu search, where different time-scale memory cycles are used as a metaheuristic to guide a hill climbing search method out of local optima and locate the globally optimum solution. Results are presented which illustrate the effectiveness of the method on mathematical test functions. In addition to these test functions, some results are presented for real problems in hydraulic circuit design by linking the method to the Bath fp dynamic simulation software. In one such example the solutions obtained are compared to those found using simple steady state calculations.

INDEX-MAXMINER: A NEW MAXIMAL FREQUENT ITEMSET MINING ALGORITHM

2008 ◽  
Vol 17 (02) ◽  
pp. 303-320 ◽  
Author(s):  
WEI SONG ◽  
BINGRU YANG ◽  
ZHANGYAN XU
Keyword(s):  
Search Strategy ◽  
Search Space ◽  
Frequent Itemset ◽  
Breadth First Search ◽  
Hybrid Search ◽  
Itemset Mining ◽  
Frequent Item ◽  
Enumeration Tree ◽  
Frequent Items ◽  
Mining Algorithms

Because of the inherent computational complexity, mining the complete frequent item-set in dense datasets remains to be a challenging task. Mining Maximal Frequent Item-set (MFI) is an alternative to address the problem. Set-Enumeration Tree (SET) is a common data structure used in several MFI mining algorithms. For this kind of algorithm, the process of mining MFI's can also be viewed as the process of searching in set-enumeration tree. To reduce the search space, in this paper, a new algorithm, Index-MaxMiner, for mining MFI is proposed by employing a hybrid search strategy blending breadth-first and depth-first. Firstly, the index array is proposed, and based on bitmap, an algorithm for computing index array is presented. By adding subsume index to frequent items, Index-MaxMiner discovers the candidate MFI's using breadth-first search at one time, which avoids first-level nodes that would not participate in the answer set and reduces drastically the number of candidate itemsets. Then, for candidate MFI's, depth-first search strategy is used to generate all MFI's. Thus, the jumping search in SET is implemented, and the search space is reduced greatly. The experimental results show that the proposed algorithm is efficient especially for dense datasets.

scholarly journals Accelerating backtrack search with a best-first-search strategy

2014 ◽  
Vol 24 (4) ◽  
pp. 901-916
Author(s):  
Zoltán Ádám Mann ◽  
Tamás Szép
Keyword(s):  
Search Strategy ◽  
Search Algorithm ◽  
Search Space ◽  
New Approach ◽  
Backtrack Search ◽  
Speed Up ◽  
Hard Problems ◽  
Best First Search ◽  
Problem Instances ◽  
Np Hard Problems

Abstract Backtrack-style exhaustive search algorithms for NP-hard problems tend to have large variance in their runtime. This is because “fortunate” branching decisions can lead to finding a solution quickly, whereas “unfortunate” decisions in another run can lead the algorithm to a region of the search space with no solutions. In the literature, frequent restarting has been suggested as a means to overcome this problem. In this paper, we propose a more sophisticated approach: a best-firstsearch heuristic to quickly move between parts of the search space, always concentrating on the most promising region. We describe how this idea can be efficiently incorporated into a backtrack search algorithm, without sacrificing optimality. Moreover, we demonstrate empirically that, for hard solvable problem instances, the new approach provides significantly higher speed-up than frequent restarting.

Sign in / Sign up

Export Citation Format

Share Document