Learning With Case-Injected Genetic Algorithms

Louis, Sushil J.; McDonnell, John R.

doi:10.1109/tevc.2004.823466

Cited by 147 publications

(107 citation statements)

References 16 publications

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“…In the field of genetic algorithms (Holland, 1975), the work done by Louis and McDonnell (2004) resembles our contribution most. In their case injected genetic algorithm (CIGAR), experience is stored in the form of solutions to problems solved earlier ('cases').…”

Section: Motivations and Related Workmentioning

confidence: 94%

Cross-task code reuse in genetic programming applied to visual learning

Jaśkowski¹,

Krawiec²,

Wieloch³

2014

International Journal of Applied Mathematics and Computer Science

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We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognized objects exhibit visual similarity.

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Section: Motivations and Related Workmentioning

confidence: 94%

Cross-task code reuse in genetic programming applied to visual learning

Jaśkowski¹,

Krawiec²,

Wieloch³

2014

International Journal of Applied Mathematics and Computer Science

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“…The usage of this classifier in other domains is a future work. S. J. Louis and J. McDonnell [24] provided a frame work for evaluating machine learning systems. S. Hill, J. Newell, and C. O'Riordan [25] proposed an inversion operator in a basic genetic algorithm and also compare the effectiveness.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Optimal Genetic Model to Investigate the User Usage Behaviour on World Wide Web

R¹,

Kumari²

2013

IJDKP

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“…RELATED RESEARCH Some attempts have been made to produce RTS players that are capable of adaptive behavior. Louis et al [6], [7] combined EAs with memories to produce strategies online in the game. While only trained offline in this paper, our SOM could serve this purpose, too.…”

Section: Self-organizing Maps For Unit Selectionmentioning

confidence: 99%

Intelligent anti-grouping in real-time strategy games

Beume

Hein

Naujoks

et al. 2008

2008 IEEE Symposium on Computational Intelligence and Games

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Abstract-Assembling suitable groups of fighting units to combat incoming enemy groups is a tactical necessity in realtime strategy (RTS) games. Furthermore it heavily influences future strategic decisions like unit building. Here, we demonstrate how to efficiently (offline) solve the problem of finding matches for the current enemy group(s) based on self-organizing maps (SOMs), powered by a simple evolutionary algorithm. The concept is implemented and thoroughly experimentally investigated in the RTS game Glest. We show that the offline learning is reliable and can be sped up considerably by employing a very simple substitute objective function instead of game simulations, making it a nearly universal, simple, and transparent technique.

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Learning With Case-Injected Genetic Algorithms

Cited by 147 publications

References 16 publications

Cross-task code reuse in genetic programming applied to visual learning

Cross-task code reuse in genetic programming applied to visual learning

An Intelligent Optimal Genetic Model to Investigate the User Usage Behaviour on World Wide Web

Intelligent anti-grouping in real-time strategy games

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