Program Code Mutation with Viability-Guided Genetic Search
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Solution Overview
Problem
Existing genetic algorithm systems are limited in mutating computer program code, leading to inefficiencies due to high non-viable code generation and computationally expensive fitness assessment, especially for large populations of complex programs.
Innovation Solution
A specialized genetic algorithm system that mutates computer program code with safeguards to ensure viability, using CPUs and GPUs for efficient assessment and modification, applying scoring functions to optimize solutions and generate viable offspring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If random or quasi-random mutation operations are used to generate mutated computer program code, then code diversity is improved, but the percentage of non-viable code increases
Solution Approach 1:
The system performs preliminary actions by maintaining a code template with placeholder elements that define the structural framework before mutation occurs. This preliminary structure ensures that even before mutation, the code has a viable foundation. The placeholder elements are strategically positioned to maintain syntactic correctness and logical flow, so that subsequent mutations are more likely to produce viable offspring code rather than completely random non-viable code.
Solution Approach 2:
The system applies local quality by allowing mutations to occur only at specific locations within the code structure - namely at the placeholder elements - rather than throughout the entire codebase. This localized approach to mutation ensures that critical structural elements remain intact while still introducing sufficient diversity through targeted modifications at designated mutation points.
2Measurement precision
If large populations of complex computer programs are assessed for fitness, then solution quality is improved, but computational cost increases
Solution Approach 1:
The system segments the fitness assessment process into distinct components: structural validation (checking syntax and basic correctness), functional evaluation (assessing whether the code performs the desired function), and performance optimization (evaluating efficiency metrics). This segmentation allows the system to efficiently filter out non-viable code through quick structural checks before investing computational resources in more expensive functional and performance assessments, thereby reducing overall computational cost while maintaining assessment accuracy.
3Manufacturing precision
If mutation operations modify computer program code, then solution optimization is improved, but code viability decreases
Solution Approach 1:
The system establishes a pre-defined code template with placeholder elements that serve as safe mutation targets before any optimization mutations occur. This preliminary structural framework ensures that mutations are confined to designated areas that are less critical to code viability, allowing aggressive optimization mutations elsewhere in the code without compromising overall functionality.
Solution Approach 2:
The placeholder elements act as intermediaries between the mutation operation and the critical code structure. Instead of mutations directly impacting vital code sections, they first interact with the placeholder elements which serve as buffers or mediators. This intermediary layer protects the core code structure from potentially viability-breaking mutations while still allowing optimization to occur through the placeholder mechanisms.
Data Source
AI summary
A genetic algorithm system generates a set of computer programs and executes a process for assessment and conditional modification of the set, repeating the process over a plurality of generations to mutate the population of solutions over time. At each generation, the system scores each program in the set to generate a respective primary score adjustment, a respective secondary score adjustment, and a respective current score. If a current score for a program is less than or equal to a first threshold, the system removes the computer program from the set. If the current score is greater than or equal to a second threshold, the system modifies the computer program to generate one or more offspring programs for use in subsequent generations. If a primary score adjustment for a program is greater than or equal to a third threshold, the system selects the computer program for performance of a task.