Genetic Network Programming with Rules

Ye, Fengming; Yu, Lu; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

doi:10.20965/jaciii.2009.p0016

Cited by 3 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, In the theoretical aspect, GNP with subroutines [13] introduces subroutines into GNP which serve as good building blocks and improves its performance. GNP with Rule Chains [14] optimizes GNP by using node transition information to replace worse individuals. Meanwhile, GNP is combined with other techniques such as reinforcement learning [9], Ant Colony Optimization (ACO) [15] and Particle Swarm Optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Genetic Network Programming with updating rule accumulation

Wang

Mabu

Hirasawa

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

Self Cite

View full text Add to dashboard Cite

Conventional evolutionary computation methods aim to find elite individuals as the optimal solutions. The rule accumulation method tries to find good experiences from individuals throughout the generations and store them as decision rules, which is regarded as solutions. Genetic Network Programming (GNP) is competent for dynamic environments because of its directed graph structure, reusability of nodes and partially observable processes. A GNP based rule accumulation method has been studied and applied to the stock trading problem. However, with the changing of dynamic environments, the old rules in the rule pool are incompetent for guiding agent's actions, thus updating these rules becomes necessary. This paper proposes a new method to update the accumulated rules in accordance with the environment changes. Sarsa-learning which is a good on-line learning policy is combined with off-line evolution to generate better individuals and update the rules in the rule pool. Tileworld problem which is an excellent benchmark for multi-agent systems is used as the simulation environment. Simulation results demonstrate the efficiency and effectiveness of the proposed method in dealing with the changing environments.

show abstract

Section: Introductionmentioning

confidence: 99%

Genetic Network Programming with updating rule accumulation

Wang

Mabu

Hirasawa

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Genetic Network Programming with Rule Chains [12] proposed a method to replace worse GNP individuals by using the extracted rule chains. Also, we could combine the GNP methods with other techniques, such as: reinforcement learning [8], Ant Colony Optimization (ACO) [13] and the Particle Swarm Optimization(PSO).…”

Section: Introductionmentioning

confidence: 99%

Genetic Network Programming with generalized rule accumulation

Wang

Mabu

Meng

et al. 2010

IEEE Congress on Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

Genetic Network Programming(GNP) is a newly developed evolutionary computation method using a directed graph as its gene structure, which is its unique feature. It is competent for dealing with complex problems in dynamic environments and is now being well studied and applied to many real-world problems such as: elevator supervisory control, stock price prediction, traffic volume forecast and data mining, etc. This paper proposes a new method to accumulate evolutionary experiences and guide agent's actions by extracting and using generalized rules. Each generalized rule is a stateaction chain which contains the past information and the current information. These generalized rules are accumulated and updated in the evolutionary period and stored in the rule pool which serves as an experience set for guiding new agent's actions. We designed a two-stage architecture for the proposed method and applied it to the Tile-world problem, which is an excellent benchmark for multi-agent systems. The simulation results demonstrated the efficiency and effectiveness of the proposed method in terms of both generalization ability and average fitness values and showed that the generalized rule accumulation method is especially remarkable when dealing with non-markov problems.

show abstract