evolutionary computation exam 2

multiple vehicle routing main idea

MX1 crossover

MX2 crossover

multiple vehicle routing three phases

family competition algorithm

On the Influence of GVR in Vehicle Routing what it is

On the Influence of GVR in Vehicle Routing generic crossover

On the Influence of GVR in Vehicle Routing specific crossover

On the Influence of GVR in Vehicle Routing representation of chromosome

A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Window main ideas

A Hybrid genetic Algorithm for the vehicle Routing Problem with Time Windows chromosome representation

schema theorem terms and meanings

Edge Coloring Grouping GA

Edge Coloring chromosome layout

edge coloring evaluation and crossover

edge coloring two part chromosomes

object part and grouping part

CHC genetic algorithm differences from SGA

CHC genetic algorithm lncest prevention

CHC genetic algorithm hamming distance

forging optimal solutions edge coloring approach

forging optimal solutions edge coloring: how is the initial solution generated

kick function definition and results

forging optimal solutions edge coloring solution representation

forging optimal solutions edge coloring fitness function

forging optimal solutions edge coloring perturbation function

text labels summary

text labels fitness function

text labels chromosome representation

text labels distance factor

text labels how is masking determined

text labels how does crossover work (with masking and uniform crossover)

Near-optimal triangulation chromosome representation

near-optimal triangulation crossover operator

near-optimal triangulation mutation operator

near-optimal triangulation parallel GA; migration interval and migration rate

near-optimal triangulation: why did we consider removing mutation? was it a good idea?

near optimal triangulation: how does the greedy algorithm for triangulation work?

u-shaped assembly line: two types

type I has fixed maximum cycle-time and the goal is to minimize the number of stations. type II has a fixed number of stations and the goal is to minimize the maximum cycle time. ordered 2-point crossover. no mutation. take a simple traditional line and make it into a U-shaped line.

subpopulations k-way: overview

subpopulations k-way: dealing with infeasibles

subpopulations k-way: X, Y, and Z crossovers

subpopulations k-way: random vs bias

subpopulations k-way: fixed vs osc

subpopulations k-way conclusions