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.

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.

(Formerly ZPRE-Implementation-6G). Repo demonstrates that adaptive interference cancellation fails - not due to insufficient model capacity - but due to the rate and structure of discontinuities in the signal.

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.

Dual‑Helix Resonance Neural Architecture

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.

Intelligent adaptive sorting engine using entropy analysis for optimal algorithm selection across 32/64-bit integers and floating-point data.

High-Performance 2D Gaussian Splatting Renderer for Adaptive Image Representation and Compression

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.

OGSim is a modular Go-based framework for simulating and benchmarking container scheduling strategies using both real Docker and simulated environments.

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。

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.