ES.md

Exhausive search

Overview

Exhausive search, a brutal way to traverse all the possible solution by incremental method, in order to gain the optimal solution.

• pros:

  1. Easy to understand/ implement

• cons:

  1. Waste a lot of computing resource
  2. Unable to reach the opimal soluiton when number getting large

Pseduocode

#(I)Initialization
count = 0 #[0,0,...,0.0,0]
Local_fitness = Global_fitness = Fitness(count)

while not met termination condition:

    #(T)Transition
    count += 1
    Neghbor(count) = p #[0,0,...,0,0,1]

    #(E)Evaluaiton
    Local_fitness = Fitness(p)

    #(D)Determination
    if Local_fitness > Global_fitness:
        Global_fitness = Local_fitness

return GLobal_fitness

• Transition: find next solution (neighbor solution) by incremental method
• Evaluation: count the number of 1 bits in the solution
• Determination: compare with global optimal, update if it gain better evaluation

Flowchart

Instructions for running on local machine

1. packages used in this projects:

   • numpy==1.24.2
   • matplotlib==3.7.1
   • pandas==1.5.3

2. Execution

   python -m Algorithm.ES

3. Folder organiation

   • Each algorithm will generate two files:
     • {filename}.png: show the trend/process of certain algo.
     • {filename}.csv: record every global optimal in every iterations
   • Check all the result in result folder
   •