An algorithm that implements intelligence based on a Method pool (a collection containing multiple types of functions). 一种基于方法池(包含多种类型的函数的集合)实现智能的算法
An emotion-driven optimizer that feels loss and navigates accordingly.
This repository contains the implementation of an enhanced NSGA-II algorithm for solving the Flexible Job Shop Scheduling Problem (FJSP), focusing on multi-objective optimization. Developed as part of the Bio-Inspired Artificial Intelligence course project at the University of Trento.
An emotion-driven optimizer that feels loss and navigates accordingly.
🧠 Thread-safe adaptive compression decision system - learns when to skip ineffective compression using lock-free algorithms.
Comprehensive analysis of Adaptive PSO vs Standard PSO on CEC2017 benchmarks. APSO demonstrates 15-40% better solution quality and 20-60% faster convergence across 30 test functions (10-100 dimensions). Foundation for real-world optimization applications.
Bio-Inspired Wireless Optimization for 6G ISAC — adaptive interference cancellation, benchmarking, and standards-aligned integration tools
Alpha Drift is an experimental platform for developing, testing, and analyzing advanced AI-driven decision-making algorithms, with a focus on adaptive learning, real-time data processing, and web3 trading automation.
A non-standard, fractal-inspired sorting algorithm with adaptive multi-pivot partitioning and k-way heap merging. Achieves near O(n log log n) performance in ideal cases.
OGSim is a modular Go-based framework for simulating and benchmarking container scheduling strategies using both real Docker and simulated environments.
Push_Swap is a 42 School project that involves sorting data using a limited set of stack operations, where the goal is to arrange a sequence of integers in ascending order using two stacks (stack A and stack B) with the fewest possible moves.
This protocol defines a meta-cognitive structure enabling systems to monitor, evaluate, and refine their own learning processes. It enhances adaptability and decision accuracy in AI, particularly in contexts requiring self-assessment and feedback loops. 本プロトコルは、システムが自身の学習過程を監視・評価・改善できるメタ認知的構造を定義します。自己評価とフィードバックループを要する環境において、AIの適応性と判断精度を向上させます。
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