Making IDEA-ARIMA Efficient in Dynamic Constrained Optimization Problems

Filipiak, Patryk; Lipiński, Piotr

doi:10.1007/978-3-319-16549-3_71

Cited by 3 publications

(2 citation statements)

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“…A proactive variant of IDEA, named mIDEA-ARIMA, was introduced in [2]. It applies the anticipation mechanism based on Auto-Regressive Integrated Moving Average (ARIMA) [1] model in order to predict future values of a fitness function using past observations.…”

Section: Algorithmmentioning

confidence: 99%

“…The contribution of this paper is twofold. Firstly, an adaptation of the modified Infeasibility Driven Evolutionary Algorithm with the anticipation mechanism based on AutoRegressive Integrated Moving Average Model (abbreviated mIDEA-ARIMA) [2] is proposed. As a result the process of maintaining the desired pose of a robotic arm is improved due to predictions of the most probable future landscapes that allow for acting prior to incoming changes.…”

Section: Introductionmentioning

confidence: 99%

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Infeasibility Driven Evolutionary Algorithm with the Anticipation Mechanism for the Reaching Goal in Dynamic Constrained Inverse Kinematics

Filipiak

Michalak

Lipiński

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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A dynamic version of the Inverse Kinematics problem addresses the two main objectives: One objective is to find a configuration of joints such that a desired pose and orientation can be reached by a robotic arm. Another one is to preserve this state in a continuously changing environment. In this paper a reaching goal in dynamic constrained Inverse Kinematics is considered where either a target point to be reached or locations of obstacles or both can change in time. The Infeasibility Driven Evolutionary Algorithm is applied for an exploration of the set of possible joint angles configurations in every moment. Additionally, the anticipation mechanism based on Auto-Regressive Integrated Moving Average Model is used in order to speed up an adaptation process so that a population of candidate solutions can be directed in advance towards the most probable future global optima.

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Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%