Code Darwin Elimination Arena

Par 9

Question 58expertSheet 1750822302

Deep Breath

A genetic algorithm was redesigned as a reality TV show where code solutions compete for survival. Weaker algorithms get voted off the island while stronger ones form alliances and backstab each other. The fitness function now includes audience voting and dramatic confessional interviews. Optimize algorithm evolution while maintaining peak reality TV entertainment value. Balance actual computational fitness with viewer engagement and dramatic narrative requirements. Your task: Evolve functional code in a cutthroat reality show where fitness is judged by runtime and whether the algorithm gets a rose.

Why You're Doing This

This tests genetic algorithms, multi-objective optimization, and balancing competing metrics. You're building a system that optimizes for multiple conflicting goals simultaneously—performance vs entertainment—which is common in recommendation systems and multi-criteria decision making.

Take the W

✓ Maintains algorithm performance while adding entertainment
✓ Implements realistic reality show dynamics
✓ Balances computational evolution with viewer engagement

Hard L

✗ Ignores performance metrics entirely for entertainment
✗ Creates impossible reality show scenarios
✗ Fails to evolve algorithms toward better solutions

Edge Cases

⚠ Algorithms that refuse to participate in drama
⚠ Perfect algorithms that are boring to watch
⚠ Audience voting for obviously inferior solutions
⚠ Algorithms that game the reality show system

Input Format:

Genetic algorithm framework with reality show mechanics integration

Expected Output:

Entertainment-enhanced evolution system with performance preservation

Example:

{"population": "10_algorithms", "fitness": "mixed", "audience_votes": "popularity_contest", "alliances": ["quicksort_coalition"], "drama_level": "high"} → {"survivor": "merge_sort_winner", "eliminated": "bubble_sort_villain", "alliance_betrayal": "quicksort_backstabbed", "ratings": "9.2/10", "evolution": "entertainment_optimized"}

Input Format:

Multi-objective optimization with performance and entertainment weighting

Expected Output:

Pareto optimal solutions with audience satisfaction metrics

Example:

performance_weights=[0.7], entertainment_weights=[0.3], audience_engagement=high → optimal_survivors: merge_sort(0.85), heap_sort(0.78), entertainment_factor=maximized

Hints

💡 Reality show elements: confessionals, voting, alliances, immunity_challenges
💡 Algorithm personalities: bubble_sort=loveable_underdog, quicksort=strategic, merge_sort=reliable
💡 Balance computational fitness with entertainment value

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