词条 | Autoconstructive evolution |
释义 |
Importance of autoconstructive evolutionAutoconstructive evolution is a good platform for answering theoretical questions about the evolution of evolvability. Preliminary evidence suggests that the way in which offspring are generated changes substantially over the course of evolution.[11] By studying these patterns, we can begin to understand how evolving systems organize themselves to evolve faster. Ultimately, such an understanding could allow us to improve our ability to solve problems with evolutionary computation. This increased ability for the process of self-replication to evolve is also thought to be important for recreating the open-ended evolutionary process observed on earth[12] Examples of autoconstructive evolutionTierra and AvidaA relatively simple form of autoconstruction occurs in systems such as Tierra and Avida. In these systems, programs replicate themselves by allocating space in memory for their offspring and then looping over all of the instructions in their genome and copying each into the newly allocated space.[13] This is autoconstruction in that the programs are responsible for determining what code ends up in the offspring. Programs most commonly make exact copies of themselves, with changes being introduced exclusively through mutation events. In principle, however, programs can compose a wide range of possible offspring by only copying a subset of their genomes. PushGPPushGP is a genetic programming system which evolves code written in the Push language.[8] Push is a stack-based language designed for easy use in genetic programming, in which every variable type (e.g. strings, integers, etc.) has its own stack. All variables are stored on the stack associated with their type. One of the variable types is executable Push code. As a result, this language design allows for rich autoconstructive evolution by treating all code left on the code stack at the end of program execution as the program's offspring.[14] Using this approach, programs have complete control over the offspring programs that they create. References1. ^{{Cite journal|last=Ekárt|first=Anikó|date=2014-03-01|title=Emergence in genetic programming|journal=Genetic Programming and Evolvable Machines|language=en|volume=15|issue=1|pages=83–85|doi=10.1007/s10710-013-9199-4|issn=1389-2576|url=http://publications.aston.ac.uk/20778/1/Emergence_in_genetic_programming.pdf}} 2. ^{{Cite book |last=Miller |first=Julian F. |url=https://books.google.com/?id=sFJROKdtvC0C&pg=PA10&dq=Autoconstructive#v=onepage&q=Autoconstructive&f=false |title=Cartesian Genetic Programming |date=2011 |pages=10 |publisher=Springer Science & Business Media |isbn=978-3642173103}} 3. ^{{Cite thesis|last=Rahim|first=Adzni Abdul|title=A parametric study on reproductive competence in auto-constructive artificial life|date=2008|degree=Masters|publisher=Universiti Malaysia Sabah|url=http://eprints.ums.edu.my/9406/|doi=}} 4. ^{{Cite book|title=Proceedings of the Evo20 Workshop, AISB|last=Ryser-welch|first=Patricia|last2=Miller|first2=Julian F.|publisher=|year=2014|isbn=|location=|pages=|chapter=A review of hyper-heuristic frameworks|citeseerx = 10.1.1.563.9564}} 5. ^{{Cite book|last=Rahim|first=A. 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1 : Evolutionary biology |
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