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AHS
2006
IEEE
142views Hardware» more  AHS 2006»
15 years 7 months ago
On-Chip Evolution Using a Soft Processor Core Applied to Image Recognition
To increase the flexibility of single-chip evolvable hardware systems, we explore possibilities of systems with the evolutionary algorithm implemented in software on an onchip pr...
Kyrre Glette, Jim Torresen, Moritoshi Yasunaga, Yo...
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
15 years 5 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
ML
2000
ACM
244views Machine Learning» more  ML 2000»
15 years 1 months ago
Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning
A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombinat...
Ryszard S. Michalski
CEC
2010
IEEE
15 years 2 months ago
A parallel framework for multi-objective evolutionary optimization
This work focuses on the development of a parallel framework method to improve the effectiveness and the efficiency of the obtained solutions by Multi-objective Evolutionary Algori...
Dipankar Dasgupta, David Camilo Becerra Romero, Al...
CEC
2007
IEEE
15 years 3 months ago
A novel general framework for evolutionary optimization: Adaptive fuzzy fitness granulation
— Computational complexity is a major challenge in evolutionary algorithms due to their need for repeated fitness function evaluations. Here, we aim to reduce number of fitness f...
Mohsen Davarynejad, Mohammad R. Akbarzadeh-Totonch...