The coreworld: Emergence and evolution of cooperative structures in a computational chemistry

Rasmussen, Steen; Knudsen, Carsten; Feldberg, Pasmus; Hindsholm, Morten

doi:10.1016/0167-2789(90)90070-6

Cited by 87 publications

(56 citation statements)

References 6 publications

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“…Self-modification has also been considered in artificial organisms as they were examined in the field of Artificial Life. Major contributions were made by the introduction of Coreworld [46] and the TIERRA [47] and its variants [1]. In this line of work, the emphasis is, however, more on the observation of emergent effects in self-modifying systems than on their use in computation.…”

Section: Review Of Self-modificationmentioning

confidence: 99%

Developments in Cartesian Genetic Programming: self-modifying CGP

Harding

Miller

Banzhaf

2010

Genet Program Evolvable Mach

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Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to p.

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Section: Review Of Self-modificationmentioning

confidence: 99%

Developments in Cartesian Genetic Programming: self-modifying CGP

Harding

Miller

Banzhaf

2010

Genet Program Evolvable Mach

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“…Objective-free evolution as well as self-replication have been studied in Artificial Life since Rasmussen's [15] and Ray's [16] work. Such research primarily investigates evolutionary dynamics in the absence of tasks, but as a result of implicit or environmental criteria that impact the ability to spread genomes through the population.…”

Section: Related Workmentioning

confidence: 99%

Combining Conflicting Environmental and Task Requirements in Evolutionary Robotics

Haasdijk

2015

2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems

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Abstract-The MONEE framework endows collective adaptive robotic systems with the ability to combine environment-and task-driven selection pressures: it enables distributed online algorithms for learning behaviours that ensure both survival and accomplishment of user-defined tasks. This paper explores the trade-off between these two requirements that evolution must establish when the task is detrimental to survival. To this end, we investigate experiments with populations of 100 simulated robots in a foraging task scenario where successfully collecting resources negatively impacts an individual's remaining lifetime. We find that the population remains effective at the task of collecting pucks even when the negative impact of collecting a puck is as bad as halving the remaining lifetime. A quantitative analysis of the selection pressures reveals that the task-based selection exerts a higher pressure than the environment.

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“…In 1989, Steen Rasmussen at Los Alamos National Laboratory created a "reserve" for computer viruses inside of a computer [5], by creating virtual CPUs that executed programs written in Redcode, a type of assembly language used in a computer game called "CoreWars", where the objective is to write code that invades and takes over another computer's memory. It turned out that the best strategy to win this game was to write self-replicating programs, and these were used by Rasmussen to inoculate that special space inside of the computer.…”

Section: Digital Lifementioning

confidence: 99%

Ab initio modeling of ecosystems with artificial life

Adami¹

2002

Natural Resources Modeling

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Artificial Life provides the opportunity to study the emergence and evolution of simple ecosystems in real time. We give an overview of the advantages and limitations of such an approach, as well as its relation to individual-based modeling techniques. The Digital Life system Avida is introduced and prospects for experiments with ab initio evolution (evolution "from scratch"), maintenance, as well as stability of ecosystems are discussed.

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The coreworld: Emergence and evolution of cooperative structures in a computational chemistry

Cited by 87 publications

References 6 publications

Developments in Cartesian Genetic Programming: self-modifying CGP

Developments in Cartesian Genetic Programming: self-modifying CGP

Combining Conflicting Environmental and Task Requirements in Evolutionary Robotics

Ab initio modeling of ecosystems with artificial life

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