An Adaptive Multiobjective Genetic Algorithm with Fuzzy <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2724395pt" id="M1" height="8.14084pt" version="1.1" viewBox="-0.0657574 -7.8684 7.1246 8.14084" width="7.1246pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-100" d="M383 397C383 424 344 448 299 448C244 448 172 409 132 375C66 319 23 227 23 146C23 42 74 -12 146 -12C208 -12 298 30 359 103L343 124C315 95 248 48 192 48C145 48 111 85 111 163C111 228 129 294 151 330C171 363 201 401 241 401C275 401 302 384 325 356C332 347 339 344 348 348C373 360 383 381 383 397Z"/></g></svg>-Means for Automatic Data Clustering

<div>Pareto optimal front obtained by the proposed ADNSGA2-FCM algorithm for Iris data set and Wine data set.</div>

Mathematical Problems in Engineering

fig8

Figure 8

Figure 8: An Adaptive Multiobjective Genetic Algorithm with Fuzzy <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2724395pt" id="M1" height="8.14084pt" version="1.1" viewBox="-0.0657574 -7.8684 7.1246 8.14084" width="7.1246pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-100" d="M383 397C383 424 344 448 299 448C244 448 172 409 132 375C66 319 23 227 23 146C23 42 74 -12 146 -12C208 -12 298 30 359 103L343 124C315 95 248 48 192 48C145 48 111 85 111 163C111 228 129 294 151 330C171 363 201 401 241 401C275 401 302 384 325 356C332 347 339 344 348 348C373 360 383 381 383 397Z"/></g></svg>-Means for Automatic Data Clustering 

Mathematical Problems in Engineering

An Adaptive Multiobjective Genetic Algorithm with Fuzzy -Means for Automatic Data Clustering

Figure 8