Genetic Algorithm

[rajeev1729]

I want to implement cocomo model using Genetic Algorithm for parameter tunning of a b c and d.
formula is Effort Applied (E) = a(KLOC)b [ man-months ].

[buran]

Sounds interesting project, not that I understand the subject. What have you tried? Post your code in python tags, full traceback in error tags in case you get exception. Ask specific questions. Given this may require very specific knowledge you may need to elaborate further on what your goal is.

[Larz60+]

https://github.com/Saurabh0907/Cocomo-Mo...-Algorithm

[rajeev1729]

from math import sin, cos, pi
from gaft import GAEngine
from gaft.components import BinaryIndividual,decimal_individual
from gaft.components import Population
from gaft.operators import TournamentSelection
from gaft.operators import UniformCrossover
from gaft.operators import FlipBitBigMutation
# Analysis plugin base class.
from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis
# Built-in best fitness analysis.
from gaft.analysis.fitness_store import FitnessStore
from gaft.analysis.console_output import ConsoleOutput
# Define population.
indv_template = BinaryIndividual(ranges=[(0, 5), (0.1, 5),(-0.5, 2.5),(0, 25)], eps=0.001)
#indv_template = decimal_individual((0, 5), (0.1, 5),(-0.5, 2.5),(0, 25))
population = Population(indv_template=indv_template, size=50).init()
# Create genetic operators.
#selection = RouletteWheelSelection()
selection = TournamentSelection()
crossover = UniformCrossover(pc=0.95, pe=0.5)
mutation = FlipBitBigMutation(pm=0.01, pbm=0.55, alpha=0.6)
# Create genetic algorithm engine.
# Here we pass all built-in analysis to engine constructor.
engine = GAEngine(population=population, selection=selection,
                  crossover=crossover, mutation=mutation,
                  analysis=[ConsoleOutput, FitnessStore])
# Define fitness function.
def effort_applied(a,b,c,d, kloc, me):
    return a * (kloc ** b) + c * me + d
import pandas as pd
dataset = pd.read_csv('anil18.csv')
kloc = dataset.iloc[:18, 0].values
me= dataset.iloc[:18, 1].values
target = dataset.iloc[:18, 2].values
rows=len(kloc[:18])
mmre=[]
vaf=[]
@engine.fitness_register
def fitness(indv):
    a,b,c,d = indv.solution
    Effort=[]
    for k in range(rows):
        E = effort_applied(a,b,c,d, kloc[k], me[k])
        Effort.append(E)
    
    dif = []
    for i in range(rows):
        dd = abs(target[i] - Effort[i]) / target[i]
        dif.append(dd)
    mmre.append(sum(dif) / 18)
    return float(min(mmre))
   
    from statistics import variance
    d = []
    for i in range(18):
        a = (target[i] - Effort[i])
        d.append(a)
    vd=variance(d)
    vy=variance(target[:18])
    va=(1-vd/vy)*100
    vaf.append(va)
    # print(y[:18])
    return float(max(vaf))
if '__main__' == __name__:
    engine.run(ng=500)