The Ecosystem Simulator is a sandbox-based game designed to simulate evolution in a virtual environment. It features natural selection, genetic mutation, and adaptation, creating dynamic ecosystems populated by evolving organisms. Players can observe and interact with complex behaviours, traits, and strategies as populations evolve over generations. The simulator offers a fascinating insight into the mechanisms driving evolutionary processes, suitable for both educational purposes and scientific exploration.
This project was developed as part of the A Level OCR NEA H446/03 Computer Science coursework, achieving a score of 69/70 marks.
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Dynamic Ecosystems: Simulate natural selection, genetic mutation, and adaptation.
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Customizable Parameters: Adjust starting populations for various organisms (e.g., wolves, deer, plants) and simulation time.
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Real-time Data Visualization: Observe population changes over time (though the final graph display was simplified to statistics due to time constraints).
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Free Camera View: Freely navigate the environment to observe organisms and their interactions.
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Organism Attributes: Each organism possesses unique attributes like health, hunger, thirst, and stamina, influencing their survival and behaviour.
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Organism Behaviours: Organisms exhibit various behaviours such as hunting, eating, drinking, searching for water, mating, and searching for mates.
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Genetic Inheritance & Mutation: Offspring inherit genetic traits from parents with a chance of random mutations, driving evolutionary changes.
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Player-Controlled Organism: Take direct control of a selected organism to influence its movement and observe its automated behaviours.
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Interactive Menus: User-friendly main menu, controls menu, pause menu, and simulation initialization settings.
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End Screen Statistics: A summary of simulation results, including final populations and total time length.
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3D Low Poly Graphics: A simplified, blocky art style for efficient rendering and clear focus on simulation mechanics.
This project is licensed under the MIT License.
Course: A Level OCR NEA H446/03 Computer Science
Academic Year: 2024/25
Final Score: 69/70 (A*)