Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)

Hirasawa, Kotaro; Okubo, Masashi; Katagiri, Hideki; Hu, Jinglu; Murata, Junichi

doi:10.1109/cec.2001.934337

Cited by 123 publications

(64 citation statements)

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“…Other examples of graph-based GP typically contain sequentially updating nodes (e.g., [33], [20] and [43]). Schmidt and Lipson [42] have recently demonstrated a number of benefits from graph encodings over traditional trees, such as reduced bloat and increased computational efficiency.…”

Section: Graph Representationsmentioning

confidence: 99%

Fuzzy dynamical genetic programming in XCSF

Preen

Bull

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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Abstract-A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic Networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such fuzzy dynamical systems within XCSF to solve two well-known test problems.

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Section: Graph Representationsmentioning

confidence: 99%

Fuzzy dynamical genetic programming in XCSF

Preen

Bull

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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“…When the problem is complex, the tree size may bloat excessively. Genetic Network Programming (GNP) with a directed graph structure, is proposed to overcome the disadvantages of GP [13], [14]. The aim of developing GNP is to deal with dynamic environments efficiently by using the directed graph structure which has more general representation ability than that of trees, and the inherently equipped functions in it.…”

Section: Genetic Network Programmingmentioning

confidence: 99%

“…As a result, JCMSI 0002/10/0302-0121 c 2009 SICE it could be effective because it works like the Automatically Defined Functions (ADFs). That is, the reusability of nodes makes GNP's structure more compact than that of GP [13].…”

Section: Introductionmentioning

confidence: 99%

Genetic Network Programming with Reconstructed Individuals

Mabu

Wang

et al. 2010

SICE Journal of Control, Measurement, and System Integration

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: A lot of research on evolutionary computation has been done and some significant classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), and Evolution Strategies (ES) have been studied. Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solution. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc., and GNP has obtained some outstanding results. Focusing on the GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP-RI). The aim of GNP-RI is to balance the exploitation and exploration of GNP, that is, to strengthen the exploitation ability by using the exploited information extensively during the evolution process of GNP and finally obtain better performances than that of GNP. In the proposed method, the worse individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worse individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving a good education. In this paper, GNP-RI is applied to the tile-world problem which is an excellent bench mark for evaluating the proposed architecture. The performance of GNP-RI is compared with that of the conventional GNP. The simulation results show some advantages of GNP-RI demonstrating its superiority over the conventional GNPs.

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“…However, GP has been used for generating graphs for Artificial Neural Networks [12], bond graphs [18], electronic circuits [15], and algorithm structures [17]. Genetic Network Programming [11] deals with the evolution of graph structures in which the numbers of nodes and their functional behaviour is fixed. Graph structured program evolution (GRAPE) as introduced by [17] is a GP technique using graph structures for generating computer programs with branches and loops.…”

Section: Ga Approaches To Process Miningmentioning

confidence: 99%

A genetic programming approach to business process mining

Turner

Tiwari

Mehnen

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process flowchart. As business processes become more and more complex there is a need for managers to understand the processes they already have in place. To undertake such a task manually would be extremely time consuming so the practice of process mining attempts to automatically reconstruct the correct representation of a process based on a set of process execution traces. This paper outlines an alternative approach to business process mining utilising a Genetic Programming (GP) technique coupled with a graph based representation. The graph based representation allows greater flexibility in the analysis of process flowchart structure and offers the possibility of mining complex business processes from incomplete or problematic event logs. A number of event logs have been mined by the GP technique featured in this paper and the results of the experimentation point towards the potential of this novel process mining approach.

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Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)

Cited by 123 publications

References 1 publication

Fuzzy dynamical genetic programming in XCSF

Fuzzy dynamical genetic programming in XCSF

Genetic Network Programming with Reconstructed Individuals

A genetic programming approach to business process mining

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