Genetic Algorithm Dependency Chain Healing for Combinatorial Optimization
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Solution Overview
Problem
Genetic algorithms face challenges in solving multi-objective combinatorial optimization problems due to irreducible complexity, where dependencies between subsystems or technology options can confound evolutionary operations, leading to exclusion of viable solutions and making it difficult to explore the entire solution space efficiently.
Innovation Solution
A method and system that build and verify dependency chains, remove trivial dependencies, and recursively specify technology options to ensure consistency, allowing the genetic algorithm to heal incomplete dependencies and build viable configurations, thereby mitigating irreducible complexity and improving the accuracy of solving combinatorial optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dependencies between subsystems are included in the genetic algorithm to ensure realistic solutions, then solution practicality is improved, but evolutionary operations are confounded and viable solutions are excluded
Solution Approach 1:
The patent applies preliminary action by pre-processing dependencies before the genetic algorithm execution. The system identifies and categorizes dependencies (hard constraints, soft constraints, preferences) and pre-computes compatibility information. This allows the GA to work with pre-validated solution candidates that already satisfy dependency requirements, preventing the confounding of evolutionary operations while maintaining solution practicality.
Solution Approach 2:
The patent introduces an intermediary layer between the genetic algorithm and the dependency constraints. This intermediary consists of constraint handlers and compatibility checkers that translate complex dependencies into GA-friendly representations. The intermediary filters and transforms dependency information into formats that do not confound evolutionary operations, allowing both realistic solutions and effective evolution to coexist.
2Adaptability or versatility
If the number of subsystems and technology options increases to improve system complexity, then solution comprehensiveness is improved, but the search space grows combinatorially making enumeration impossible
Solution Approach 1:
The patent applies segmentation by dividing the massive combinatorial search space into manageable subspaces based on subsystem boundaries and dependency groups. The system processes each subsystem independently where possible, and only combines them where dependencies require coordination. This segmentation reduces the effective search space from combinatorial (10^20 to 10^100) to tractable sizes while maintaining comprehensive solution coverage.
Solution Approach 2:
The patent applies local quality by allowing different parts of the search space to be explored with different strategies and levels of detail. High-priority subsystems with critical dependencies receive more thorough exploration, while independent subsystems use simpler sampling. This localized approach to search intensification maintains solution comprehensiveness without requiring exhaustive enumeration of the entire combinatorial space.
3Reliability
If complex dependencies are enforced to ensure system consistency, then solution validity is improved, but genetic operations such as mutation and crossover become harder to satisfy
Solution Approach 1:
The patent applies dynamics by making the constraint enforcement mechanism adaptive during genetic operations. The system dynamically adjusts between strict constraint satisfaction and flexible constraint handling based on the operation type (mutation vs. crossover), the specific dependency involved, and the current population state. This dynamic approach maintains solution validity while preserving genetic operation feasibility by not uniformly blocking all operations.
Solution Approach 2:
The patent applies parameter changes by modifying constraint satisfaction parameters during the genetic algorithm execution. The system changes constraint weights, enforcement strictness, and validation thresholds based on the evolutionary stage and population diversity. This allows complex dependencies to be enforced with varying degrees of rigidity, maintaining solution validity while accommodating the probabilistic nature of genetic operations.
Data Source
AI summary
A system and method for designing a physical system using a genetic algorithm includes building a plurality of data structures necessary to build, heal, and verify a plurality of dependency chains; ensuring that multiple dependencies in a respective one of the plurality of dependency chains are represented correctly; removing any dependencies that will be trivially satisfied at random; in response to determining that one or more dependencies is consistent with another dependency, considering one or more combinations of dependencies; and building configurations that satisfy the dependencies and combinations of dependencies by associating the dependencies and combinations of dependencies with selected technology options and recursively specifying and/or revising additional technology options that are consistent with the dependencies or combinations of dependencies, until a configuration is fully specified.