Effective Data Logging and Code Maintenance in Assignments
Learn how to efficiently manage data logging requirements and maintain code quality in assignments to avoid penalties for unfixed issues. Detailed guidelines and examples provided for handling data logging and code maintenance effectively throughout your assignments.
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You will be penalized for any unfixed issues with any code that gets reused in later assignments. You have to fix everything in .py files based on our feedback. You should NOT wait for feedback on each assignment before starting the next one. Grading these assignments is incredibly time-consuming and even with 3 TAs we cannot get feedback to you quickly enough for that. You usually do NOT need your old code to be fixed until you start actually running your experiment and collecting data to analyze for your report.
Logging Requirements One file for stats One value per run (30 values) Best fitness value ever seen during each individual run For 1b s green portion, we write this to a file for you One file for best solution Genotype of the best solution ever seen out of all runs One (or 30) file(s) for log NOT the logs we generate, analyze, and save for you One line for each generation, showing: Evaluation count Mean fitness of current adult population Max fitness of current adult population
DATA LOGGING Example for ? = 999,955 & ? = 10 Best solution out of all runs: 12, 3, 0 25, 12, 3 20, 6, 2 Experiment log file (can instead be one file per run): Eval Run 1 999955 999965 999975 999985 999995 1000005 Local adult average Local adult best Solution had the highest global fitness of all 30 runs 12.2341 12.3765 12.5632 12.4561 12.9841 12.6452 18 18 19 19 22 20 Global best from each run (stats): Local Global 23 15 32 Run 2 999955 999965 8.3942 9.1693 11 13
