We present a statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata. Because No Free Lunch theorems di...
This paper compares three common evolutionary algorithms and our modified GA, a Distributed Adaptive Genetic Algorithm (DAGA). The optimal approach is sought to adapt, in near rea...
Thomas F. Clayton, Leena N. Patel, Gareth Leng, Al...
This paper focuses on an approach to modeling shapes through the use of evolutionary optimization or genetic algorithms for functionally represented geometric objects. This repres...
The notion of partial and evolutionary specification has gained attention both in research and industry in the last years. While many people regard this just as a process issue, w...
Abstract— We recast the problem of unconstrained continuous evolutionary optimization as inference in a fixed graphical model. This approach allows us to address several pervasi...
Christopher K. Monson, Kevin D. Seppi, James L. Ca...