Bridging the Gap Between Evolvable Hardware and Industry Using Cartesian Genetic Programming

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@InCollection{Vasicek:2017:miller,

• author = "Zdenek Vasicek",
• title = "Bridging the Gap Between Evolvable Hardware and Industry Using Cartesian Genetic Programming",
• booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday",
• publisher = "Springer",
• year = "2017",
• editor = "Susan Stepney and Andrew Adamatzky",
• volume = "28",
• series = "Emergence, Complexity and Computation",
• chapter = "2",
• pages = "39--55",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW",
• isbn13 = "978-3-319-67996-9",
• DOI = "doi:10.1007/978-3-319-67997-6_2",
• abstract = "Advancements in technology developed in the early nineties have enabled researchers to successfully apply techniques of evolutionary computation in various problem domains. As a consequence, a new research direction referred to as evolvable hardware (EHW) focusing on the use of evolutionary algorithms to create specialized electronics has emerged. One of the goals of the early pioneers of EHW was to evolve complex circuits and overcome the limits of traditional design. Unfortunately, evolvable hardware found itself in a critical stage around 2010 and a very pessimistic future for EHW-based digital circuit synthesis was predicted. The problems solved by the community were of the size and complexity of that achievable in fifteens years ago and seldom compete with traditional designs. The scalability problem has been identified as one of the most difficult problems that researchers are faced with and it was not clear whether there existed a path forward that would allow the field to progress. Despite that, researchers have continued to investigate how to overcome the scalability issues and significant progress has been made in the area of evolutionary synthesis of digital circuits in recent years. The goal of this chapter is to summarize the progress in the evolutionary synthesis of gate-level digital circuits, and to identify the challenges that need to be addressed to enable evolutionary methods to penetrate into industrial practice.",
• notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624",

}

Genetic Programming entries for Zdenek Vasicek

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