Bioinformatics & ML Optimization
Chaotic Lévy and adaptive restart enhance the Manta Ray foraging optimizer for gene feature selection
This study introduces CLA-MRFO, an adaptive variant of the Manta Ray Foraging Optimization (MRFO) algorithm, designed to overcome challenges in high-dimensional search spaces. It incorporates chaotic Lévy flight, phase-aware memory, and an entropy-informed restart strategy. Evaluated on CEC'17 benchmarks, CLA-MRFO achieved superior performance, reducing mean error on 23 of 29 functions by 31.7% and demonstrating high stability. Applied to leukemia gene selection, it identified ultra-compact, biologically coherent gene subsets, achieving a mean F1-score of 0.953 across six classification models under stringent cross-validation. While effective for binary classification, its generalizability to multi-class diagnostic contexts revealed constraints, indicating context-dependent biomarkers.
Executive Impact
Key performance indicators highlighting the practical benefits and advancements of CLA-MRFO.
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Performance Gain
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Functions Won
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F1-Score (Leukemia)
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Variance Across Runs
Deep Analysis & Enterprise Applications
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Benchmark Dominance
CLA-MRFO achieved a remarkable 31.7% average performance gain over the next best algorithm on the CEC'17 benchmark suite. This highlights its superior ability to navigate complex, high-dimensional search spaces and find optimal solutions efficiently. The algorithm demonstrated the lowest mean error on 23 out of 29 functions, showcasing its robust global optimization capabilities.
31.7%
Average performance gain over next best algorithm
CLA-MRFO Algorithm Flow
The core of CLA-MRFO lies in its synergistic integration of adaptive components. Chaotic Lévy flight modulation dynamically adjusts step sizes, promoting global exploration and preventing premature convergence. Phase-aware memory banks preserve diverse elite solutions, ensuring a rich set of candidates for exploitation. An entropy-informed restart strategy injects diversity when stagnation is detected, without compromising convergence stability. This multi-pronged approach ensures a dynamic balance between exploration and exploitation.
Enterprise Process Flow
Initialize Population & Memory
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Generate Chaotic Values
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Foraging Strategies (Cyclone/Chain)
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Local Search (Adaptive)
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Evaluate & Update Best Solution
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Check Stagnation & Restart
Leukemia Gene Feature Selection
CLA-MRFO demonstrated significant practical utility in a high-dimensional leukemia gene selection task. It successfully identified ultra-compact subsets of biologically coherent genes that play established roles in leukemia pathogenesis. These selected features enabled robust and generalizable classification performance, with F1-scores exceeding 0.95 across multiple machine learning models under stringent cross-validation. This highlights CLA-MRFO's potential for biomarker discovery.
Case Study: Biomedical Research Lab
Client: Biomedical Research Lab
Challenge: Identifying minimal, highly discriminative gene subsets for Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) classification from high-dimensional microarray data, addressing the 'curse of dimensionality' and ensuring biological relevance.
Solution: CLA-MRFO was applied to a high-dimensional leukemia gene expression dataset (7,129 genes, 72 samples) using a nested 5-fold cross-validation protocol. It identified ultra-compact gene subsets (<5% of original features) by minimizing misclassification error and promoting compactness.
Results: The selected gene subsets enabled a mean F1-score of 0.953 ± 0.012 across six state-of-the-art classification models (Gradient Boosting, XGBoost, LightGBM, CatBoost, Random Forest, SVM). These genes were biologically coherent, including known oncogenic drivers like TCF3, MYB, and PARP1, demonstrating clinical relevance. Performance was consistent with less than 5% variance across runs for binary classification, but generalizability to multi-class (ALL, AML, Healthy) was limited, indicating context-dependency.
Scalability & Robustness Comparison
The algorithm exhibits excellent scalability, with sub-linear growth in runtime as problem dimensionality increases. Its robustness is demonstrated by low variance across independent runs and consistent performance on diverse benchmark functions. While it introduces moderate computational overhead due to its adaptive components, the performance gains significantly outweigh this cost, making it suitable for practical, high-dimensional optimization.
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Your AI Implementation Roadmap
A typical phased approach to integrate advanced AI optimization into your enterprise, ensuring success and value.
Phase 1: Initial Assessment & Data Preparation
Comprehensive review of problem scope, data characteristics, and objectives. Includes data preprocessing and initial exploratory analysis.
Phase 2: CLA-MRFO Model Configuration
Tailoring CLA-MRFO parameters, chaotic maps, memory settings, and restart strategies to the specific optimization task, leveraging sensitivity analysis insights.
Phase 3: Iterative Optimization & Validation
Execution of CLA-MRFO for feature selection or parameter optimization, employing rigorous cross-validation and statistical validation to ensure robust and generalizable results.
Phase 4: Interpretability & Deployment
Analysis of selected features or optimized parameters for biological/domain relevance. Integration of the optimized model into production systems or further research.
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