The Survival of the Fittest Route: Genetic Algorithms in Shipping and Sustainability скачать в хорошем качестве

The Survival of the Fittest Route: Genetic Algorithms in Shipping and Sustainability

How can the study of ant colonies and evolutionary theory help us ensure the resilience and sustainability of global shipping? In this episode, host Amelia Jabry is joined by Professor Adam Sobey (Mission Director for Sustainability at the Alan Turing Institute) and Senior Applied Scientist Dr. Przemyslaw (Slaw) Grudniewski from Theyr. Together, they explore the ‘evolution’ of shipping route optimisation - from early concepts proposed by Alan Turing to cutting-edge Multi-Objective Genetic Algorithms. Discover how these ‘survival of the fittest’ models are being used to navigate the complex world of charter party agreements, fuel efficiency, and autonomous vessels. They also dive into the environmental impact of rerouting, discussing how a 1% change in fuel consumption can protect vital megafauna like whales, and what the melting Arctic means for the future of global trade.   Chapter Markers 0:30 | Co-Host Introduction: Professor Adam Sobey • Introduction of Adam Sobey, Mission Director for Sustainability at the Alan Turing Institute and Professor at the University of Southampton. 1:20 | Guest Introduction: Dr. Przemyslaw (Slaw) Grudniewski • Introduction of Slava, Senior Applied Scientist atTheyr • The history of Adam and Slava’s collaboration, starting from Slava's PhD in 2015. 4:30 | The Path to Genetic Algorithms • Why the team focused on genetic algorithms, including the influence of a talk at the University of Bristol on co-evolution mechanisms. 5:00 | Why Shipping Matters: The Ever Given Incident • The significance of global trade by sea (80-90%) and the 2021 Suez Canal blockage by theEver Given. 6:20 | The Sustainability Imperative • Shipping currently accounts for 2-3% of world emissions, emphasizing the massive need for reduced costs and improved sustainability. 7:15 | Defining Genetic Algorithms • Explaining unsupervised learning algorithms based on "survival of the fittest" and evolutionary mechanics. 8:40 | Applying Evolutionary Principles to Route Optimisation • How routes are treated as individuals that create "offspring" through crossover and mutation. 10:20 | Multi-Objective Genetic Algorithms • "There is no one best route"—balancing conflicting goals like voyage time vs. fuel consumption. • Explaining why multi-objective approaches provide a set of optimal solutions rather than a single answer. 11:00 | Charter Party Agreements & Alternative Fuels • The complexity of "rental agreements" (charter parties) and the shift toward net-zero fuels like ammonia, hydrogen, and nuclear. 12:20 | The Rise of Fully Autonomous Vessels • Navigating the challenges of crewless ships and how they allow for real-time route adjustments. 13:30 | Sustainability Benefits of Autonomy • Removing crew-related weight can lead to estimated fuel reductions of around 20%. 14:40 | Safety and Regulation • The role of the Alan Turing Institute and Lloyd’s Register in developing standards and validation for autonomous systems. 16:15 | Risks: Cyber Threats and Bad Actors • Addressing piracy, cybersecurity risks, and the safety of alternative fuel sources. 18:00 | Why Genetic Algorithms Win • Comparing genetic algorithms against local search methods like A* and Dijkstra for complex, real-world problems. 19:00 | Top Performance: cMLSGA • ThecMLSGA (Convolutional Multi-Level Selection Genetic Algorithm) and its 7-8% improvement over other models. • This represents a saving of 50 to 380 tonnes of fuel per day for large vessels. 20:20 | History: From Alan Turing to Today • How the field traces back to Turing’s 1948 ideas of "child-like" intelligence that learns and evolves. 22:20 | Ants, Tribes, and Co-Evolution • Using the study of ant colonies and human tribal behaviour to understand collective fitness and reproduction. 23:50 | Scaling Solutions through Collectives • Applying the concept of "collectives" to solve large-scale optimisation problems through collaboration. 25:25 | Multi-Level Selection • How "groups of individuals" (collectives) can compete and work together to look at different objectives simultaneously. 26:20 | Collective vs. Convergence-Based Algorithms • Why maintaining diversity in a population is more effective than focusing on a single "perfect" solution too early. • Diversity provides better and more informed choices with the data at hand...