---
name: Algorithm Improvement
about: Suggest improvements to evolutionary algorithms, genetic operators, or optimization strategies
title: "[Algorithm]: "
labels: ["algorithm", "enhancement", "research"]
assignees:
- manasp21
---
## Algorithm Enhancement Proposal
### Problem Domain
<!-- What specific area of the evolutionary algorithm needs improvement? -->
- [ ] **Selection Strategies** (tournament, roulette wheel, rank-based)
- [ ] **Crossover Operators** (single-point, multi-point, uniform, semantic)
- [ ] **Mutation Mechanisms** (random, guided, adaptive, context-aware)
- [ ] **Fitness Evaluation** (multi-objective, constraint handling, dynamic fitness)
- [ ] **Population Management** (diversity maintenance, niching, speciation)
- [ ] **Convergence Detection** (early stopping, plateau detection, multi-criteria)
- [ ] **Parallelization** (concurrent evaluation, distributed evolution)
- [ ] **Memory Optimization** (solution storage, history tracking)
- [ ] **Other**: _____________________
### Current Limitation
<!-- Describe the current limitation or suboptimal behavior -->
**Problem Description:**
<!-- Detailed description of what's not working optimally -->
**Current Implementation:**
<!-- Brief description of how it currently works -->
**Performance Impact:**
- **Population sizes affected:**
- **Generation counts affected:**
- **Time complexity:** O(?)
- **Space complexity:** O(?)
- **Typical runtime:** ___ seconds/minutes
### Proposed Algorithm Enhancement
**New Approach:**
<!-- Describe your proposed algorithmic improvement -->
**Mathematical Foundation:**
<!-- Include mathematical formulation if applicable -->
```
// Pseudocode or mathematical notation
```
**Expected Improvements:**
- **Performance:** Expected speedup of ___x
- **Quality:** Expected improvement in solution quality
- **Convergence:** Expected faster/more reliable convergence
- **Scalability:** Better handling of large populations/complex problems
### Implementation Strategy
**High-Level Design:**
<!-- How would this be implemented in the existing codebase? -->
**Code Structure Changes:**
- [ ] New functions in `EvolutionaryEngine`
- [ ] Modifications to existing algorithms
- [ ] New configuration parameters
- [ ] API changes required
- [ ] Database/storage changes
**Backward Compatibility:**
- [ ] Fully backward compatible
- [ ] Requires migration/configuration update
- [ ] Breaking change (worth it because _____)
### Research and References
**Academic Sources:**
<!-- Link to papers, books, or research that supports this approach -->
- Paper 1: [Title](URL) - Brief relevance description
- Paper 2: [Title](URL) - Brief relevance description
**Existing Implementations:**
<!-- Examples from other systems or libraries -->
- System 1: How they implement it
- System 2: Their approach and results
**Benchmarks and Comparisons:**
<!-- If you have benchmark data comparing approaches -->
### Testing and Validation
**Test Cases:**
<!-- How should this enhancement be tested? -->
1. **Unit Tests:**
- Algorithm correctness
- Edge case handling
- Performance regression testing
2. **Integration Tests:**
- Compatibility with existing tools
- End-to-end evolution workflows
- Multi-generation stability
3. **Performance Tests:**
- Benchmark against current implementation
- Scalability testing with large populations
- Memory usage analysis
**Success Metrics:**
- [ ] ___% improvement in convergence time
- [ ] ___% improvement in solution quality
- [ ] Handles ___x larger populations
- [ ] ___% reduction in memory usage
- [ ] Maintains compatibility with existing workflows
### Example Usage
**Configuration:**
```json
{
"problemStatement": "Optimize neural network architecture",
"consistencyChecks": [
"Network must achieve >95% accuracy",
"Architecture must be trainable in <1 hour",
"Model size must be <10MB"
],
"algorithmConfig": {
"newFeature": {
"enabled": true,
"parameter1": "value1",
"parameter2": 0.5
}
}
}
```
**Expected Workflow:**
```typescript
// How the new algorithm would be used
const evolution = new EvolutionaryEngine();
evolution.startEvolution(config);
// ... existing workflow but with improvements
```
### Implementation Timeline
**Phase 1** (Estimated: ___ days/weeks)
- [ ] Core algorithm implementation
- [ ] Basic unit tests
- [ ] Documentation
**Phase 2** (Estimated: ___ days/weeks)
- [ ] Integration with existing system
- [ ] Performance optimization
- [ ] Comprehensive testing
**Phase 3** (Estimated: ___ days/weeks)
- [ ] User documentation
- [ ] Migration guides (if needed)
- [ ] Release preparation
### Risk Assessment
**Technical Risks:**
- [ ] Algorithm complexity may impact maintainability
- [ ] Performance improvements may not match expectations
- [ ] Integration challenges with existing code
**Mitigation Strategies:**
- Phased implementation with rollback options
- Extensive testing and benchmarking
- Feature flags for gradual rollout
### Questions for Discussion
1. **Theoretical Questions:**
- Are there edge cases where this algorithm might perform poorly?
- How does this interact with existing evolutionary operators?
2. **Practical Questions:**
- Should this be configurable or always-on?
- What default parameters make sense?
- How do we handle migration for existing users?
3. **Research Questions:**
- Are there variants of this algorithm worth considering?
- What future research directions does this enable?
### Additional Context
<!-- Any other context, related issues, or relevant information -->
**Related Issues:**
- #123 - Related performance issue
- #456 - User request for this type of improvement
**Domain Expertise:**
<!-- Your background or expertise in this area -->
**Implementation Interest:**
- [ ] I would like to implement this myself
- [ ] I can provide algorithmic guidance
- [ ] I can help with testing and validation
- [ ] I can provide research support
- [ ] I need help with implementation
