About this Research Topic
Digital evolution is an applied branch of Artificial Life. In this evolutionary computation framework, self-replicating computer programs (i.e., digital organisms) evolve within a user-defined computational environment. Computational approaches developed to study evolution in action satisfy the three essential requirements for evolution to occur: replication, heritable variation, and differential fitness.
For example, in Avida (i.e., the most widely used computational approach for studying evolution) differences in fitness among digital organisms arise through competition for the limited resources of memory space and central processing unit (CPU) time.
A digital organism consists of a sequence of instructions (i.e., its genome) and a virtual CPU, which executes these instructions. Some of these instructions are involved in copying an organism's genome, which is the only way the organism can pass on its genetic material to future generations. To reproduce, a digital organism must copy its genome instruction by instruction into a new region of memory. The copying process occasionally introduces mutations including point mutations, insertions, and deletions. In addition to the instructions required for replication (i.e. viability), the instruction set includes basic arithmetic operations (such as addition, multiplications, and bit-shifts) as well as the logic operator "nand" that are executed on binary numbers taken from the environment through input-output instructions. When the output of processing these numbers equals the result of a specific Boolean logic operation, such as the AND and OR Boolean functions, the digital organism is said to have a trait represented by that logic operation. Interactions among digital organisms (i.e., the ecology of the system) occur through trait matching. Different mechanisms for mapping trait matching to interactions can be implemented depending on the antagonistic or mutualistic nature of the interaction. The inclusion of ecological interactions in digital systems enables new research avenues: investigations using self-replicating computer programs complement laboratory efforts by broadening the breadth of viable experiments focused on the emergence and diversification of coevolving interactions in complex communities. This cross-disciplinary research program provides fertile grounds for new collaborations between computer scientists, ecologists, and evolutionary biologists.
Although the field is now mature enough, some biologists are still quite reluctant to embrace it. Some editors are still skeptical about the utility of artificial life approaches for direct comparison to empirical studies as the mapping between biology and algorithm is not always clear, leaving too many possible explanations for observed discrepancies. A collection of original research papers, written for biologists on the potential of digital evolution to help us understand the ecology and evolutionary biology, has the potential to change this picture overnight.
This Research Topic covers all aspects of ecology and evolutionary biology addressed by using digital evolution software platforms: e.g., the evolution of genome architecture, gene regulatory networks, robustness, evolvability, complexity, phenotypic plasticity, adaptive radiations, ecological interactions, cooperation, and the evolution of sex.
For example, in Avida (i.e., the most widely used computational approach for studying evolution) differences in fitness among digital organisms arise through competition for the limited resources of memory space and central processing unit (CPU) time.
A digital organism consists of a sequence of instructions (i.e., its genome) and a virtual CPU, which executes these instructions. Some of these instructions are involved in copying an organism's genome, which is the only way the organism can pass on its genetic material to future generations. To reproduce, a digital organism must copy its genome instruction by instruction into a new region of memory. The copying process occasionally introduces mutations including point mutations, insertions, and deletions. In addition to the instructions required for replication (i.e. viability), the instruction set includes basic arithmetic operations (such as addition, multiplications, and bit-shifts) as well as the logic operator "nand" that are executed on binary numbers taken from the environment through input-output instructions. When the output of processing these numbers equals the result of a specific Boolean logic operation, such as the AND and OR Boolean functions, the digital organism is said to have a trait represented by that logic operation. Interactions among digital organisms (i.e., the ecology of the system) occur through trait matching. Different mechanisms for mapping trait matching to interactions can be implemented depending on the antagonistic or mutualistic nature of the interaction. The inclusion of ecological interactions in digital systems enables new research avenues: investigations using self-replicating computer programs complement laboratory efforts by broadening the breadth of viable experiments focused on the emergence and diversification of coevolving interactions in complex communities. This cross-disciplinary research program provides fertile grounds for new collaborations between computer scientists, ecologists, and evolutionary biologists.
Although the field is now mature enough, some biologists are still quite reluctant to embrace it. Some editors are still skeptical about the utility of artificial life approaches for direct comparison to empirical studies as the mapping between biology and algorithm is not always clear, leaving too many possible explanations for observed discrepancies. A collection of original research papers, written for biologists on the potential of digital evolution to help us understand the ecology and evolutionary biology, has the potential to change this picture overnight.
This Research Topic covers all aspects of ecology and evolutionary biology addressed by using digital evolution software platforms: e.g., the evolution of genome architecture, gene regulatory networks, robustness, evolvability, complexity, phenotypic plasticity, adaptive radiations, ecological interactions, cooperation, and the evolution of sex.
Keywords: Computational Biology, Evolutionary Ecology, Genotype-Phenotype Map, Evolvability, Robustness
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