Differential evolution for parameterized procedural woody plant models reconstruction
Applied Soft Computing
MECCA: a robust low-overhead PUF using embedded memory array
CHES'11 Proceedings of the 13th international conference on Cryptographic hardware and embedded systems
Proceedings of the 49th Annual Design Automation Conference
Identification of recovered ICs using fingerprints from a light-weight on-chip sensor
Proceedings of the 49th Annual Design Automation Conference
COSADE'12 Proceedings of the Third international conference on Constructive Side-Channel Analysis and Secure Design
ClockPUF: physical unclonable functions based on clock networks
Proceedings of the Conference on Design, Automation and Test in Europe
Information Sciences: an International Journal
An accurate probabilistic reliability model for silicon PUFs
CHES'13 Proceedings of the 15th international conference on Cryptographic Hardware and Embedded Systems
A write-time based memristive PUF for hardware security applications
Proceedings of the International Conference on Computer-Aided Design
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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This paper presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals and the tuning process. The general idea is based on individual evaluations according to played games through several generations and different environments. We introduce a new history mechanism which uses an auxiliary population containing good individuals. This new mechanism ensures that good individuals remain within the evolutionary process, even though they died several generations back and later can be brought back into the evolutionary process. In such a manner the evaluation of individuals is improved and consequently the whole tuning process.