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We investigate fundamental decisions in the design of instruction setarchitectures for linear genetic programs that are used as both model systemsin evolutionary biology and underlying solution representations in evolutionarycomputation. We subjected digital organisms with each tested architecture toseven different computational environments designed to present a range ofevolutionary challenges. Our goal was to engineer a general purposearchitecture that would be effective under a broad range of evolutionaryconditions. We evaluated six different types of architectural features for thevirtual CPUs: (1) genetic flexibility: we allowed digital organisms to moreprecisely modify the function of genetic instructions, (2) memory: we providedan increased number of registers in the virtual CPUs, (3) decoupled sensors andactuators: we separated input and output operations to enable greater controlover data flow. We also tested a variety of methods to regulate expression: (4)explicit labels that allow programs to dynamically refer to specific genomepositions, (5) position-relative search instructions, and (6) multiple new flowcontrol instructions, including conditionals and jumps. Each of these featuresalso adds complication to the instruction set and risks slowing evolution dueto epistatic interactions. Two features (multiple argument specification andseparated I/O) demonstrated substantial improvements int the majority of testenvironments. Some of the remaining tested modifications were detrimental,thought most exhibit no systematic effects on evolutionary potential,highlighting the robustness of digital evolution. Combined, these observationsenhance our understanding of how instruction architecture impacts evolutionarypotential, enabling the creation of architectures that support more rapidevolution of complex solutions to a broad range of challenges.

Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures