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eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-02-06T17:11:09.049Z","updated_at":"2026-02-06T17:11:12.008Z","avatar_url":"https://github.com/skycoin.png","language":"Go","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Installation\n\n- Install and setup go 1.14 and above.\n- Install CX (https://github.com/skycoin/cx).\n- `git pull` your CX repository so you have the latest CX version.\n- Make sure `go module` is enabled. Install go dependencies by running:\n```\n$go mod verify \n$go mod tidy\n$go mod download\n```\n\n# Benchmarks\n| Task Name              | Task Code Name      | Task Version |\n| ---------------------- |:-------------------:|:------------:| \n|  Maze                  | \"maze\"              |      1       |\n|  Constants             | \"constants\"         |      1       | \n|  Evens                 | \"evens\"             |      1       |\n|  Evens greater than 0  | \"evens\"             |      2       |\n|  Odds                  | \"odds\"              |      1       |\n|  Primes                | \"primes\"            |      1       |\n|  Composites            | \"composites\"        |      1       |\n|  Network Simulator     | \"network_simulator\" |      1       |\n|  Range                 | \"range\"             |      1       |\n\n\n### Summary\n\nThis program runs evolve with Maze. Generated programs are given multiple i32 as inputs and outputs one i32. The output is their move for the maze. \n\n### Usage\nFor more information, run\n```\ngo run main.go help \n```\n\n```\ngo run main.go \n--task=[Name of task to benchmark (in lower case--refer to task code name above)]\n--task-version=[Task vesion value, default is 1]\n--population=[Population size or number of programs generated per generation]\n--generations=[number of generations]\n--expressions=[number of expressions a generated program can have]\n--graphs=[Set true if average fitness and fittest per generation graphs will be saved] \n--ast=[Set true if best ASTs per generation will be saved]\n--use-log-fitness=[Set true if fitness will be fitness(log base 2)]\n--workers=[number of workers available to use, if not set default is 1]\n--random-search=[set true to have no mutation on individuals]\n--select-rank-cufoff=[set true if selection is select, rank, and cutoff]\n\n\u003c!-- Set if maze benchmark --\u003e\n-W=[width of the maze to solve] \n-H=[height of maze to solve]  \n--random-maze-size=[set true if generated maze for every N epochs will have random sizes (NxN 2,3,4,5,6,7, or 8)]\n--epoch-length=[Maze changes for every N generation(Example. Epochs=5, maze changes every 5 generations)]\n\n\u003c!-- Set if range benchmark --\u003e\n--upper-range=[upper range (int) for range benchmark]\n--lower-range=[lower range (int) for range benchmark]\n\n\u003c!-- Set if benchmark is either constants, evens, odds, primes, composites, range, or network simulator --\u003e\n--rounds=[Number of rounds per program]\n\n\u003c!-- Set if benchmark is constants --\u003e\n--constants-target=[target number for constants benchmark]\n\n```\n\n### Example Benchmarking\n\nFor Maze\n```\ngo run main.go --task=maze -W=2 -H=2 --random-maze-size=false --population=50 --generations=100 --expressions=30 --epoch-length=100 --graphs=true --ast=false --use-log-fitness=false --workers=1\n```\n\nFor Range\n```\ngo run main.go --task=range --upper-range=9 --lower-range=2 --rounds=10 --population=300 --generations=1000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1\n```\n\nFor Constants\n```\ngo run main.go --task=constants --rounds=10 --population=300 --generations=1000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1\n```\n\nFor Constants with explicit target\n```\ngo run main.go --task=constants --rounds=10 --constants-target=5 --population=300 --generations=1000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1\n\nNote: For constants benchmarking 0-256 range, use --constants-target=256.\n```\n\nFor Odds\n```\ngo run main.go --task=odds --rounds=10 --population=300 --generations=1000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1\n```\n\nFor Evens\n```\ngo run main.go --task=evens --rounds=10 --population=300 --generations=1000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1\n```\n\nFor Primes\n```\ngo run main.go --tasks=primes --rounds=10 --population=300 --generations=1000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1\n```\n\nFor Composites\n```\ngo run main.go --task=composites --rounds=10 --population=300 --generations=1000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1\n```\n\nFor Network Simulator\n```\n go run main.go --task=network_simulator --rounds=20 --population=300 --generations=2000 --expressions=30 --graphs=true --ast=false --use-log-fitness=false --workers=1  \n```\n\n### Notes\n1. If no arguments are specified, the program will run default values.\n\n# Benchmarking in Docker Env\n\nFirst edit the ./scripts/maze_benchmark.sh to the benchmark options needed and the number of workers to deploy.\nThen use this command:\n\nTo build and run\n```\nNAME=[Name for the docker image]\nMOUNT=[Local directory to mount the \"Result\" directory of the container, this is where the graphs and asts can be found] \nmake deploy \n```\n\nExample\n```\nNAME=testbenchmark MOUNT=/Benchmarking/Results make deploy \n```\n\nTo build and push to dockerhub\n```\nNAME=[Name for the docker image]\nmake push-docker\n```\n\nExample\n```\nNAME=testbenchmark make push-docker\n```\n\n# High priority tasks\n- [x] Maze\n- [ ] Snake\n- [ ] Tic-Tac-Toe\n- [ ] Multi-armed bandit\n- [ ] Rock, paper \u0026 scissors\n\n# Low priority tasks\n- [ ] Time-series mean estimation\n- [ ] Time-series variance estimation\n- [ ] Time-series covariance estimation\n- [ ] Stock market simulator\n- [ ] Committee of experts\n- [ ] Linear feedback shift register prediction\n- [ ] Non-linear feedback shift register prediction\n- [x] Check if a number is prime","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskycoin%2Fcx-evolves","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fskycoin%2Fcx-evolves","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskycoin%2Fcx-evolves/lists"}