Session: Knowledge - Based Approaches to Evolutionary Computation Using Cultural Algorithms, Case - Injected Genetic Algorithms, and Related Techniques for Real World Engineering Applications – 2 Sessions
Organizer:
Robert G. Reynolds
Department of Computer Science
431 State Hall
Wayne State University
Detroit, MI 48202
reynolds@CS.Wayne.EDU
1. Using Cultural Algorithms to Improve Performance in Semantic Networks
Nestor Rychtyckyj
Manufacturing and Engineering Development
AI and Expert Systems Group
Ford Motor Company
Detroit
Evolutionary computation has been successfully applied in a variety of problem domains and applications. In this paper we describe the use of a specific form of evolutionary computation known as cultural algorithms to improve the efficiency of the subsumption algorithm in semantic networks. Subsumption is the process that determines if one node in the network is a child of another node. As such, it is utilized as part of the node classification algorithm within semantic network-based applications. One method of improving subsumption efficiency is to reduce the number of attributes that need to be compared for every node without impacting the results. We suggest that a cultural algorithm approach can be used to identify these defining attributes that are most significant for node retrieval. These results can then be utilized within an existing vehicle assembly process planning application that utilizes a semantic network based knowledge base to improve the performance and reduce complexity of the network.
2. Performing Fault detection within a Complex Engineering Environment through the utilization of Chained Cultural Algorithms.
David Ostrowski
Science Laboratories
Ford Motor Company
Detroit
Software testing is extremely difficult in the context of engineering applications. Biezer made a distinction between a functional approach to testing software as opposed to a structural approach. The functional approach is considered Black Box testing and the structural White Box testing. We feel that these methods are difficult to apply since they represent deterministic approaches to complex problems which have been known to be evaluated to NP-hard. Since constantly changing environments are heuristical in nature, we suggest that the application of the White and Black Box testing methods within a Cultural Algorithm environment will present a successful approach to fault detection. In order to utilize both a functional and structural approach, two Cultural Algorithms will be applied within this tool. The first algorithm will utilize the Black Box testing by establishing an equivalence class of data through the means of maintaining a belief space over a number of populations. the equivalence class will then be passed over to the second Cultural Algorithm that will apply program slicing techniques to determine program slices from the data established within the first algorithm. The goal will be to pinpoint faults within the program design. Through the searching of the program code this approach can be considered behavioral mining of a program.
3. Learning to Assess the Quality of Genetic Programs Using Cultural Algorithms
George Cowan
Park Davis Laboratories
Michigan
The total program length of Genetic Programming (GP) solution programs can be partitioned into effective program length and several types of excess code length for which, following Angeline, we use the term "bloat". Local Bloat is the excess code length attributable to local introns, sections of code in the solution program that do not affect the results for any training input. If the training data is highly representative of the problem domain, then it is likely that local intron removal does not affect the results for any possible input. Considering code after the removal of introns, we define Global Bloat to be the excess length of code which can be represented more succinctly while using the same operators that were allowed during evolution. Finally we distinguish Representational Bloat, the excess length, after the removal of Global Bloat, of code which can be represented more succinctly using operators from an expanded set. The remaining code is the Effective Program. We explore the relationships found between these types of code length and three GP process modifications purported to reduce bloat.
4. A Cultural Algorithm Application
Robert G. Reynolds
5. Solving High Dimensional Real-valued Function Optimization Problems With Cultural Algorithms
Chan – Jin Chung
Lawrence Technological University
Department of Math and Computer Science
Southfield, Michigan
6. Learning to Identify Objects with Case-Injected Genetic Algorithms
Sushil J. Louis
Department of Computer Science
University of Nevada Reno
Reno, Nevada 89557-0148